From 38f3b6f21f03d7b76a00e91b6b2d2495736e7381 Mon Sep 17 00:00:00 2001 From: Azure Pipelines Date: Wed, 15 Nov 2023 17:21:26 +0000 Subject: [PATCH] Publish GitHub Pages [skip ci] --- ...g_Incident_Classification_with_Facet.ipynb | 8 +- ...arn_classifier_summaries_using_FACET.ipynb | 6 +- .../Classification_with_Facet.ipynb | 10 +- docs/_generated/getting_started.html | 78 ++----------- docs/_generated/release_notes.html | 109 ++++++++++-------- .../_modules/facet/simulation/viz/_style.html | 47 +++++--- .../_generated/getting_started.rst.txt | 31 ++--- .../_sources/_generated/release_notes.rst.txt | 8 ++ docs/_sources/contribution_guide.rst.txt | 4 +- docs/_sources/faqs.rst.txt | 2 +- .../Classification_with_Facet.ipynb.txt | 10 +- .../Model_simulation_deep_dive.ipynb.txt | 8 +- ...classifier_summaries_using_FACET.ipynb.txt | 6 +- ...cident_Classification_with_Facet.ipynb.txt | 8 +- docs/_static/js/versions.js | 4 +- docs/_static/pygments.css | 1 + 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docs/faqs.html | 2 +- docs/objects.inv | Bin 15334 -> 15341 bytes docs/searchindex.js | 2 +- docs/tutorial/Classification_with_Facet.html | 12 +- docs/tutorial/Classification_with_Facet.ipynb | 10 +- docs/tutorial/Model_simulation_deep_dive.html | 12 +- .../tutorial/Model_simulation_deep_dive.ipynb | 8 +- ...earn_classifier_summaries_using_FACET.html | 8 +- ...arn_classifier_summaries_using_FACET.ipynb | 6 +- ...ng_Incident_Classification_with_Facet.html | 12 +- ...g_Incident_Classification_with_Facet.ipynb | 8 +- 156 files changed, 1178 insertions(+), 1250 deletions(-) diff --git a/docs/_downloads/87bd7a5fc5edee5f7e46545ac3ff5913/Water_Drilling_Incident_Classification_with_Facet.ipynb b/docs/_downloads/87bd7a5fc5edee5f7e46545ac3ff5913/Water_Drilling_Incident_Classification_with_Facet.ipynb index 6289d3e0..5a02f025 100644 --- a/docs/_downloads/87bd7a5fc5edee5f7e46545ac3ff5913/Water_Drilling_Incident_Classification_with_Facet.ipynb +++ b/docs/_downloads/87bd7a5fc5edee5f7e46545ac3ff5913/Water_Drilling_Incident_Classification_with_Facet.ipynb @@ -29,7 +29,7 @@ " \n", "- **Enhanced Machine Learning Workflow** \n", "\n", - " FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features. \n", + " FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features. \n", "\n", "***\n", "\n", @@ -111,7 +111,7 @@ "\n", "1. Common packages (pandas, matplotlib, etc.)\n", "2. Required FACET classes (inspection, selection, validation, simulation, etc.)\n", - "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-Gamma/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-Gamma/pytools)) when using FACET" + "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-X-Official/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-X-Official/pytools)) when using FACET" ] }, { @@ -191,7 +191,7 @@ "source": [ "**sklearndf imports**\n", "\n", - "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n", + "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n", "\n", "`from sklearn.pipeline import Pipeline`\n", "\n", @@ -231,7 +231,7 @@ "source": [ "**pytools imports**\n", "\n", - "pytools (see on [GitHub](https://github.com/BCG-Gamma/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET." + "pytools (see on [GitHub](https://github.com/BCG-X-Official/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET." ] }, { diff --git a/docs/_downloads/cf9a77d79c2a3565a488450ce979e06f/Scikit-learn_classifier_summaries_using_FACET.ipynb b/docs/_downloads/cf9a77d79c2a3565a488450ce979e06f/Scikit-learn_classifier_summaries_using_FACET.ipynb index 00f821d3..02385b16 100644 --- a/docs/_downloads/cf9a77d79c2a3565a488450ce979e06f/Scikit-learn_classifier_summaries_using_FACET.ipynb +++ b/docs/_downloads/cf9a77d79c2a3565a488450ce979e06f/Scikit-learn_classifier_summaries_using_FACET.ipynb @@ -27,7 +27,7 @@ " \n", "- **Enhanced Machine Learning Workflow** \n", "\n", - " FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features. \n", + " FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features. \n", "\n", "***\n", "\n", @@ -89,7 +89,7 @@ "\n", "1. Common packages (pandas, matplotlib, sklearn, etc.)\n", "2. Required FACET classes (i.e., selection)\n", - "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)) when using FACET" + "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)) when using FACET" ] }, { @@ -151,7 +151,7 @@ "source": [ "**sklearndf imports**\n", "\n", - "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n", + "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n", "\n", "`from sklearn.pipeline import Pipeline`\n", "\n", diff --git a/docs/_downloads/f8edc62901c3352636cbd43e4d813713/Classification_with_Facet.ipynb b/docs/_downloads/f8edc62901c3352636cbd43e4d813713/Classification_with_Facet.ipynb index 633dd449..9a61e41e 100644 --- a/docs/_downloads/f8edc62901c3352636cbd43e4d813713/Classification_with_Facet.ipynb +++ b/docs/_downloads/f8edc62901c3352636cbd43e4d813713/Classification_with_Facet.ipynb @@ -31,7 +31,7 @@ " \n", "- **Enhanced Machine Learning Workflow** \n", "\n", - " FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features. \n", + " FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features. \n", "\n", "***\n", "\n", @@ -108,7 +108,7 @@ "\n", "1. Common packages (pandas, matplotlib, etc.)\n", "2. Required FACET classes (inspection, selection, validation, simulation, etc.)\n", - "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-Gamma/pytools)) when using FACET" + "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-X-Official/pytools)) when using FACET" ] }, { @@ -162,7 +162,7 @@ "source": [ "**sklearndf imports**\n", "\n", - "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n", + "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n", "\n", "`from sklearn.pipeline import Pipeline`\n", "\n", @@ -194,7 +194,7 @@ "source": [ "**pytools imports**\n", "\n", - "pytools (see on [GitHub](https://github.com/BCG-Gamma/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET." + "pytools (see on [GitHub](https://github.com/BCG-X-Official/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET." ] }, { @@ -1557,7 +1557,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For convenience when working in a non-notebook environment, all of the `Drawer`'s provided by the [pytools](https://github.com/BCG-Gamma/pytools) package also support a `style='text'` flag." + "For convenience when working in a non-notebook environment, all of the `Drawer`'s provided by the [pytools](https://github.com/BCG-X-Official/pytools) package also support a `style='text'` flag." ] }, { diff --git a/docs/_generated/getting_started.html b/docs/_generated/getting_started.html index 84d3ddb9..fe0380ac 100644 --- a/docs/_generated/getting_started.html +++ b/docs/_generated/getting_started.html @@ -261,72 +261,10 @@

Getting started with#

-../_images/Gamma_Facet_Logo_RGB_LB.svg
-

Note

-

FACET 2.0 now available!

-

FACET 2.0 brings numerous API enhancements and improvements, accelerates model -inspection by up to a factor of 50 in many practical applications, introduces a new, -more flexible and user-friendly API for hyperparameter tuning – with support for -scikit-learn’s native hyperparameter searchers – and improves the styling of all -visualizations.

-

See the release notes -for more details.

-
-

FACET is an open source library for human-explainable AI. +../_images/Gamma_Facet_Logo_RGB_LB.svg

FACET is an open source library for human-explainable AI. It combines sophisticated model inspection and model-based simulation to enable better explanations of your supervised machine learning models.

FACET is composed of the following key components:

- ---- - - - - - - - - - - - -

      

-

inspect

-

Model Inspection

-

FACET introduces a new algorithm to quantify dependencies and -interactions between features in ML models. -This new tool for human-explainable AI adds a new, global -perspective to the observation-level explanations provided by the -popular SHAP approach. -To learn more about FACET’s model inspection capabilities, see the -getting started example below.

-

      

-

sim

-

Model Simulation

-

FACET’s model simulation algorithms use ML models for -virtual experiments to help identify scenarios that optimise -predicted outcomes. -To quantify the uncertainty in simulations, FACET utilises a range -of bootstrapping algorithms including stationary and stratified -bootstraps. -For an example of FACET’s bootstrap simulations, see the -quickstart example below.

-

      

-

pipe

-

Enhanced Machine Learning Workflow

-

FACET offers an efficient and transparent machine learning -workflow, enhancing -scikit-learn’s -tried and tested pipelining paradigm with new capabilities for model -selection, inspection, and simulation. -FACET also introduces -sklearndf -[documentation] -an augmented version of scikit-learn with enhanced support for -pandas data frames that ensures end-to-end traceability of features.

-

Installation#

FACET supports both PyPI and Anaconda. @@ -364,9 +302,9 @@

QuickstartFACET documentation.

+see the FACET documentation.

Changes and additions to new versions are summarized in the -release notes.

+release notes.

Enhanced Machine Learning Workflow#

To demonstrate the model inspection capability of FACET, we first create a @@ -572,7 +510,7 @@

Model InspectionAPI reference +API reference for more detail.

@@ -630,17 +568,17 @@

ContributingGitHub form, and if you wish to do so, +GitHub form, and if you wish to do so, please open a PR addressing the issue.

We do ask that for any major changes please discuss these with us first via an issue or using our team email: FacetTeam@bcg.com.

For further information on contributing please see our -contribution guide.

+contribution guide.

License#

FACET is licensed under Apache 2.0 as described in the -LICENSE file.

+LICENSE file.

Acknowledgements#

@@ -657,7 +595,7 @@

Acknowledgements

BCG GAMMA#

If you would like to know more about the team behind FACET please see the -about us page.

+about us page.

We are always on the lookout for passionate and talented data scientists to join the BCG GAMMA team. If you would like to know more you can find out about BCG GAMMA, diff --git a/docs/_generated/release_notes.html b/docs/_generated/release_notes.html index eeb25d2e..9d60c3d6 100644 --- a/docs/_generated/release_notes.html +++ b/docs/_generated/release_notes.html @@ -173,6 +173,11 @@

  • + 2.1.1 + +
    • +
  • + 2.1.0
    • @@ -184,12 +189,12 @@
    • - + 2.0.1
    • - + 2.0.0
        @@ -262,22 +267,22 @@
        • - + 1.2.3
        • - + 1.2.2
        • - + 1.2.1
        • - + 1.2.0
          • @@ -289,17 +294,17 @@
          • - + 1.1.2
          • - + 1.1.1
          • - + 1.1.0
            • @@ -311,17 +316,17 @@
            • - + 1.0.3
            • - + 1.0.2
            • - + 1.0.1
              • @@ -354,12 +359,16 @@

              FACET 2.1#

              FACET 2.1 introduces the NativeLearnerInspector for inspecting native scikit-learn models and pipelines.

              -

              We still recommend using sklearndf models and learner pipelines along with FACET’s +

              We still recommend using sklearndf models and learner pipelines along with FACET’s LearnerSelector for hyperparameter tuning; however the new NativeLearnerInspector can be useful for inspecting models that have been trained using scikit-learn directly.

              -

              2.1.0#

              +

              2.1.1#

              +

              This is a maintenance release to catch up with FACET 2.0.1.

              +
              +
              +

              2.1.0#

              • API: new NativeLearnerInspector class for inspecting native scikit-learn regressors, classifiers, and pipelines with a regressor or classifier as the final @@ -374,19 +383,21 @@

                FACET 2.0pytools 2.0 and sklearndf 2.2, and is now -fully type-checked by mypy.

                -
                -

                2.0.1#

                +

                FACET 2.0 requires pytools 2.0 and sklearndf 2.2, and is now +fully type-checked by mypy.

                +
                +

                2.0.1#

                • API: class LearnerInspector now supports inspecting individual regressors and classifiers; it is no longer necessary to wrap them into a RegressorPipelineDF or ClassifierPipelineDF instance with empty preprocessing

                  • +

                • FIX: replace a call to method get_text_heights() of matplotlib.axes.Axes, +which is deprecated as of matplotlib 3.6

                -
                -

                2.0.0#

                +
                +

                2.0.0#

                facet.data#

                  @@ -416,17 +427,17 @@

                  facet.inspectio

                • API: LearnerInspector no longer uses learner crossfits and instead inspects models using a single pass of SHAP calculations, usually leading to performance gains of up to a factor of 50

                  • -

                • API: return LearnerInspector matrix outputs as Matrix +

                • API: return LearnerInspector matrix outputs as Matrix instances

                • API: diagonals of feature synergy, redundancy, and association matrices are now nan instead of 1.0

                  • -

                • API: the leaf order of LinkageTree objects generated by +

                • API: the leaf order of LinkageTree objects generated by feature_…_linkage methods of LearnerInspector is now the same as the -row and column order of Matrix objects returned by the +row and column order of Matrix objects returned by the corresponding feature_…_matrix methods of LearnerInspector, minimizing the distance between adjacent leaves. The old sorting behaviour of FACET 1.x can be restored using method -sort_by_weight()

                  • +sort_by_weight()

                @@ -466,11 +477,11 @@

                Other

                @@ -482,29 +493,29 @@

                FACET 1.2 -

                1.2.3#

                +
                +

                1.2.3#

                • BUILD: pin down matplotlib version to < 3.6 and scipy version to < 1.9 to ensure compatibility with pytools 1.2 and sklearndf 1.2

                -
                -

                1.2.2#

                +
                +

                1.2.2#

                • catch up with FACET 1.1.2

                -
                -

                1.2.1#

                +
                +

                1.2.1#

                -
                -

                1.2.0#

                +
                +

                1.2.0#

                • BUILD: added support for sklearndf 1.2 and scikit-learn 0.24

                • API: new optional parameter subsample in method @@ -518,8 +529,8 @@

                  1.2.0#<

                  FACET 1.1#

                  FACET 1.1 refines and enhances the association/synergy/redundancy calculations provided by the LearnerInspector.

                  -
                  -

                  1.1.2#

                  +
                  +

                  1.1.2#

                  -
                  -

                  1.1.1#

                  +
                  +

                  1.1.1#

                  • DOC: add reference to FACET research paper on the project landing page

                  • FIX: correctly count positive class frequency in UnivariateProbabilitySimulator

                  -
                  -

                  1.1.0#

                  +
                  +

                  1.1.0#

                  FACET 1.0#

                  -
                  -

                  1.0.3#

                  +
                  +

                  1.0.3#

                  • FIX: restrict package requirements to gamma-pytools 1.0 and sklearndf 1.0, since FACET 1.0 is not compatible with gamma-pytools 1.1

                  -
                  -

                  1.0.2#

                  +
                  +

                  1.0.2#

                  This is a maintenance release focusing on enhancements to the CI/CD pipeline and bug fixes.

                  • API: add support for shap 0.36 and 0.37 via a new BaseExplainer stub class

                  • FIX: apply color scheme to the histogram section in SimulationMatplotStyle

                    • -

                  • BUILD: add support for numpy 1.20

                    • +

                  • BUILD: add support for numpy 1.20

                  • BUILD: updates and changes to the CI/CD pipeline

                  -
                  -

                  1.0.1#

                  +
                  +

                  1.0.1#

                  Initial release.

                  diff --git a/docs/_modules/facet/simulation/viz/_style.html b/docs/_modules/facet/simulation/viz/_style.html index f95c03e0..f3a14b05 100644 --- a/docs/_modules/facet/simulation/viz/_style.html +++ b/docs/_modules/facet/simulation/viz/_style.html @@ -173,9 +173,10 @@

                  Source code for facet.simulation.viz._style

                   
 import logging
 from abc import ABCMeta, abstractmethod
-from typing import Any, Sequence, TextIO, Tuple, TypeVar, Union
+from typing import Any, Optional, Sequence, TextIO, Tuple, TypeVar, Union, cast
 
 from matplotlib.axes import Axes
+from matplotlib.transforms import Bbox
 from mpl_toolkits.axes_grid1 import make_axes_locatable
 from mpl_toolkits.axes_grid1.axes_divider import AxesDivider
 from mpl_toolkits.axes_grid1.axes_size import Scaled
@@ -372,27 +373,44 @@ 
                  Source code for facet.simulation.viz._style
                               y_min, y_max = main_ax.get_ylim()
             uplift_height = abs(y_max - y_min)
 
-            def _x_axis_height() -> float:
-                axis_below_size_pixels: float
-                _, axis_below_size_pixels = main_ax.get_xaxis().get_text_heights(
+            def _x_axis_height(padding: float) -> float:
+                # get the lower bound of the x axis in data space: this is the upper
+                # bound of the space below the x axis
+                x_axis_vertical_position: float = main_ax.get_ylim()[0]
+
+                # get the upper and lower vertical bound of the tick labels
+                xaxis_tight_bbox: Optional[Bbox] = main_ax.get_xaxis().get_tightbbox(
                     self.get_renderer()
                 )
-
-                y0: float
-                y1: float
-                ((_, y0), (_, y1)) = main_ax.transData.inverted().transform(
-                    ((0.0, 0.0), (0.0, axis_below_size_pixels))
+                if xaxis_tight_bbox is None:
+                    return 0.0
+
+                x_axis_labels_lower: float
+                x_axis_labels_upper: float
+                (
+                    x_axis_labels_lower,
+                    x_axis_labels_upper,
+                ) = main_ax.transData.inverted().transform(
+                    xaxis_tight_bbox.get_points()
+                )[
+                    :, 1
+                ]
+
+                # calculate the height, and pad it with the multiple of the tick
+                # label height provided in arg padding
+                return (
+                    max(x_axis_vertical_position - x_axis_labels_lower, 0)
+                    + (x_axis_labels_upper - x_axis_labels_lower) * padding
                 )
-                return abs(y1 - y0)
 
             # calculate the height of the x axis in data space; add additional padding
-            axis_below_size_data = _x_axis_height() * 1.2
+            axis_below_size_data = _x_axis_height(padding=0.5)
 
             # create the axes divider, then use it to append the new sub-axes at the
             # bottom while leaving sufficient padding in-between to accommodate the
             # main axes' x axis labels
             divider: AxesDivider = make_axes_locatable(main_ax)
-            return divider.append_axes(
+            sub_axes: Axes = divider.append_axes(
                 position="bottom",
                 size=Scaled(
                     uplift_height * SimulationMatplotStyle.__HISTOGRAM_SIZE_RATIO
@@ -403,6 +421,7 @@ 
                  Source code for facet.simulation.viz._style
                                       * (1 + SimulationMatplotStyle.__HISTOGRAM_SIZE_RATIO)
                 ),
             )
+            return sub_axes
 
         ax = _make_sub_axes()
 
@@ -415,10 +434,10 @@ 
                  Source code for facet.simulation.viz._style
                           # reduce the horizontal margin such that half a bar is to the left of the
         # leftmost tick mark (but the tick mark stays aligned with the main
         # simulation chart)
-        x_margin, _ = ax.margins()
+        x_margin, _ = cast(Tuple[float, float], ax.margins())
         ax.set_xmargin(
             max(
-                0,
+                0.0,
                 (width_bars / 2 - x_margin * (n_partitions - 1))
                 / (width_bars - (n_partitions - 1)),
             )
diff --git a/docs/_sources/_generated/getting_started.rst.txt b/docs/_sources/_generated/getting_started.rst.txt
index 443d291a..fd61bebe 100644
--- a/docs/_sources/_generated/getting_started.rst.txt
+++ b/docs/_sources/_generated/getting_started.rst.txt
@@ -4,19 +4,6 @@ Getting started with
 ====================
 .. image:: /_images/Gamma_Facet_Logo_RGB_LB.svg
 
-.. note:: **FACET 2.0 now available!**
-
-    FACET 2.0 brings numerous API enhancements and improvements, accelerates model
-    inspection by up to a factor of 50 in many practical applications, introduces a new,
-    more flexible and user-friendly API for hyperparameter tuning – with support for
-    `scikit-learn`'s native hyperparameter searchers – and improves the styling of all
-    visualizations.
-
-    See the `release notes `__
-    for more details.
-
-
-
 FACET is an open source library for human-explainable AI.
 It combines sophisticated model inspection and model-based simulation to enable better 
 explanations of your supervised machine learning models.
@@ -53,8 +40,8 @@ FACET is composed of the following key components:
 |                 | tried and tested pipelining paradigm with new capabilities for model  |
 |                 | selection, inspection, and simulation.                                |
 |                 | FACET also introduces                                                 |
-|                 | `sklearndf `__                |
-|                 | [`documentation `__]|
+|                 | `sklearndf `__                |
+|                 | [`documentation `__]|
 |                 | an augmented version of *scikit-learn* with enhanced support for      |
 |                 | *pandas* data frames that ensures end-to-end traceability of features.|
 +-----------------+-----------------------------------------------------------------------+
@@ -106,10 +93,10 @@ Quickstart
 The following quickstart guide provides a minimal example workflow to get you
 up and running with FACET.
 For additional tutorials and the API reference,
-see the `FACET documentation `__.
+see the `FACET documentation `__.
 
 Changes and additions to new versions are summarized in the
-`release notes `__.
+`release notes `__.
 
 
 Enhanced Machine Learning Workflow
@@ -348,7 +335,7 @@ removing `TCH`, and one of `TC` or `LDL` to further simplify the model and obtai
 reduced set of independent features.
 
 Please see the
-`API reference `__
+`API reference `__
 for more detail.
 
 
@@ -411,21 +398,21 @@ FACET is stable and is being supported long-term.
 
 Contributions to FACET are welcome and appreciated.
 For any bug reports or feature requests/enhancements please use the appropriate
-`GitHub form `_, and if you wish to do so,
+`GitHub form `_, and if you wish to do so,
 please open a PR addressing the issue.
 
 We do ask that for any major changes please discuss these with us first via an issue or
 using our team email: FacetTeam@bcg.com.
 
 For further information on contributing please see our
-`contribution guide `__.
+`contribution guide `__.
 
 
 License
 -------
 
 FACET is licensed under Apache 2.0 as described in the
-`LICENSE `_ file.
+`LICENSE `_ file.
 
 
 Acknowledgements
@@ -445,7 +432,7 @@ BCG GAMMA
 ---------
 
 If you would like to know more about the team behind FACET please see the
-`about us `__ page.
+`about us `__ page.
 
 We are always on the lookout for passionate and talented data scientists to join the
 BCG GAMMA team. If you would like to know more you can find out about
diff --git a/docs/_sources/_generated/release_notes.rst.txt b/docs/_sources/_generated/release_notes.rst.txt
index 45445a8d..b84e9f40 100644
--- a/docs/_sources/_generated/release_notes.rst.txt
+++ b/docs/_sources/_generated/release_notes.rst.txt
@@ -19,6 +19,12 @@ We still recommend using :mod:`sklearndf` models and learner pipelines along wit
 :class:`.NativeLearnerInspector` can be useful for inspecting models that have been
 trained using *scikit-learn* directly.
 
+2.1.1
+~~~~~
+
+This is a maintenance release to catch up with FACET |nbsp| 2.0.1.
+
+
 2.1.0
 ~~~~~
 
@@ -46,6 +52,8 @@ fully type-checked by |mypy|.
   and classifiers; it is no longer necessary to wrap them into a
   :class:`.RegressorPipelineDF` or :class:`.ClassifierPipelineDF` instance with empty
   preprocessing
+- FIX: replace a call to method ``get_text_heights()`` of :class:`matplotlib.axes.Axes`,
+  which is deprecated as of :mod:`matplotlib` |nbsp| 3.6
 
 
 2.0.0
diff --git a/docs/_sources/contribution_guide.rst.txt b/docs/_sources/contribution_guide.rst.txt
index 1f6467dd..c16306dd 100644
--- a/docs/_sources/contribution_guide.rst.txt
+++ b/docs/_sources/contribution_guide.rst.txt
@@ -296,7 +296,7 @@ The ``sphinx/source`` folder contains:
 
 
 The two key scripts are ``make.py`` and ``conf.py``. The base configuration for the
-these scripts can be found in `pytools/sphinx `_.
+these scripts can be found in `pytools/sphinx `_.
 The reason for this is to minimise code given the standardization of the documentation
 build across multiple packages.
 
@@ -452,7 +452,7 @@ The build process for the PyPI and conda distributions uses the following key
 files:
 
 - ``make.py``: generic Python script for package builds. Most configuration is imported
-  from pytools `make.py `__
+  from pytools `make.py `__
   which is a build script that wraps the package build, as well as exposing the matrix
   dependency definitions specified in the ``pyproject.toml`` as environment variables
 - ``pyproject.toml``: metadata for PyPI, build settings and package dependencies
diff --git a/docs/_sources/faqs.rst.txt b/docs/_sources/faqs.rst.txt
index fb62b425..ae554f83 100644
--- a/docs/_sources/faqs.rst.txt
+++ b/docs/_sources/faqs.rst.txt
@@ -14,7 +14,7 @@ on `stackoverflow `_.
 1. **What if I find a bug or have an idea for a new feature?**
 
     For bug reports or feature requests please use our
-    `GitHub issue tracker `_.
+    `GitHub issue tracker `_.
     For any other enquiries please feel free to contact us at FacetTeam@bcg.com.
 
 2. **How does FACET's novel algorithm calculate pairwise feature redundancy and synergy?**
diff --git a/docs/_sources/tutorial/Classification_with_Facet.ipynb.txt b/docs/_sources/tutorial/Classification_with_Facet.ipynb.txt
index 633dd449..9a61e41e 100644
--- a/docs/_sources/tutorial/Classification_with_Facet.ipynb.txt
+++ b/docs/_sources/tutorial/Classification_with_Facet.ipynb.txt
@@ -31,7 +31,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
     "\n",
     "***\n",
     "\n",
@@ -108,7 +108,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, etc.)\n",
     "2. Required FACET classes (inspection, selection, validation, simulation, etc.)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-Gamma/pytools)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-X-Official/pytools)) when using FACET"
    ]
   },
   {
@@ -162,7 +162,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
+    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
@@ -194,7 +194,7 @@
    "source": [
     "**pytools imports**\n",
     "\n",
-    "pytools (see on [GitHub](https://github.com/BCG-Gamma/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
+    "pytools (see on [GitHub](https://github.com/BCG-X-Official/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
    ]
   },
   {
@@ -1557,7 +1557,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For convenience when working in a non-notebook environment, all of the `Drawer`'s provided by the [pytools](https://github.com/BCG-Gamma/pytools) package also support a `style='text'` flag."
+    "For convenience when working in a non-notebook environment, all of the `Drawer`'s provided by the [pytools](https://github.com/BCG-X-Official/pytools) package also support a `style='text'` flag."
    ]
   },
   {
diff --git a/docs/_sources/tutorial/Model_simulation_deep_dive.ipynb.txt b/docs/_sources/tutorial/Model_simulation_deep_dive.ipynb.txt
index 6b10d66f..6a0d540d 100644
--- a/docs/_sources/tutorial/Model_simulation_deep_dive.ipynb.txt
+++ b/docs/_sources/tutorial/Model_simulation_deep_dive.ipynb.txt
@@ -40,7 +40,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.\n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.\n",
     "\n",
     "***\n",
     "\n",
@@ -117,7 +117,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, etc.)\n",
     "2. Required FACET classes (inspection, selection, validation, simulation, etc.)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-Gamma/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-Gamma/pytools)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-X-Official/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-X-Official/pytools)) when using FACET"
    ]
   },
   {
@@ -168,7 +168,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" *scikit-learn* package, we are going to use *sklearndf* (see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)). *sklearndf* is an open source library designed to address a common issue with *scikit-learn*: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. *sklearndf* retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard *scikit-learn* import:\n",
+    "Instead of using the \"regular\" *scikit-learn* package, we are going to use *sklearndf* (see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)). *sklearndf* is an open source library designed to address a common issue with *scikit-learn*: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. *sklearndf* retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard *scikit-learn* import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
@@ -193,7 +193,7 @@
    "source": [
     "**pytools imports**\n",
     "\n",
-    "pytools (see on [GitHub](https://github.com/BCG-Gamma/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
+    "pytools (see on [GitHub](https://github.com/BCG-X-Official/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
    ]
   },
   {
diff --git a/docs/_sources/tutorial/Scikit-learn_classifier_summaries_using_FACET.ipynb.txt b/docs/_sources/tutorial/Scikit-learn_classifier_summaries_using_FACET.ipynb.txt
index 00f821d3..02385b16 100644
--- a/docs/_sources/tutorial/Scikit-learn_classifier_summaries_using_FACET.ipynb.txt
+++ b/docs/_sources/tutorial/Scikit-learn_classifier_summaries_using_FACET.ipynb.txt
@@ -27,7 +27,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
     "\n",
     "***\n",
     "\n",
@@ -89,7 +89,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, sklearn, etc.)\n",
     "2. Required FACET classes (i.e., selection)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)) when using FACET"
    ]
   },
   {
@@ -151,7 +151,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
+    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
diff --git a/docs/_sources/tutorial/Water_Drilling_Incident_Classification_with_Facet.ipynb.txt b/docs/_sources/tutorial/Water_Drilling_Incident_Classification_with_Facet.ipynb.txt
index 6289d3e0..5a02f025 100644
--- a/docs/_sources/tutorial/Water_Drilling_Incident_Classification_with_Facet.ipynb.txt
+++ b/docs/_sources/tutorial/Water_Drilling_Incident_Classification_with_Facet.ipynb.txt
@@ -29,7 +29,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.       \n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.       \n",
     "\n",
     "***\n",
     "\n",
@@ -111,7 +111,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, etc.)\n",
     "2. Required FACET classes (inspection, selection, validation, simulation, etc.)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-Gamma/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-Gamma/pytools)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-X-Official/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-X-Official/pytools)) when using FACET"
    ]
   },
   {
@@ -191,7 +191,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
+    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
@@ -231,7 +231,7 @@
    "source": [
     "**pytools imports**\n",
     "\n",
-    "pytools (see on [GitHub](https://github.com/BCG-Gamma/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
+    "pytools (see on [GitHub](https://github.com/BCG-X-Official/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
    ]
   },
   {
diff --git a/docs/_static/js/versions.js b/docs/_static/js/versions.js
index 2178ec61..c87743e6 100644
--- a/docs/_static/js/versions.js
+++ b/docs/_static/js/versions.js
@@ -1,7 +1,7 @@
 const DOCS_VERSIONS = {
-    "current": "2.1.0",
+    "current": "2.1.1",
     "all": [
-        "2.1.0",
+        "2.1.1",
         "2.0.1",
         "1.2.3",
         "1.1.2",
diff --git a/docs/_static/pygments.css b/docs/_static/pygments.css
index f227e5c6..6110e9f1 100644
--- a/docs/_static/pygments.css
+++ b/docs/_static/pygments.css
@@ -22,6 +22,7 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
 .highlight .cs { color: #8f5902; font-style: italic } /* Comment.Special */
 .highlight .gd { color: #a40000 } /* Generic.Deleted */
 .highlight .ge { color: #000000; font-style: italic } /* Generic.Emph */
+.highlight .ges { color: #000000; font-weight: bold; font-style: italic } /* Generic.EmphStrong */
 .highlight .gr { color: #ef2929 } /* Generic.Error */
 .highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
 .highlight .gi { color: #00A000 } /* Generic.Inserted */
diff --git a/docs/apidoc/facet/data/facet.data.Sample.html b/docs/apidoc/facet/data/facet.data.Sample.html
index efe0a3e4..83f6c832 100644
--- a/docs/apidoc/facet/data/facet.data.Sample.html
+++ b/docs/apidoc/facet/data/facet.data.Sample.html
@@ -601,12 +601,12 @@ 
                  facet.data.SampleDataFrame.
                  
+
                  The underlying data structure is a DataFrame.
                  
 
                  Supports len(), returning the number of observations in this sample.
                  
 
                  
 
                  Parameters
                  
 
                  
 
 
                  
 
                  
-property index: pandas.Index#
                  
+property index: pandas.Index#
 
                  Row index of all observations in this sample.
                  
 
                  
 
                  Return type
                  
-
                  Index
                  
+
                  Index
                  
 
                  
 
                  
 
                  
 
 
                  
 
                  
-property target: Union[pandas.Series, pandas.DataFrame]#
                  
+property target: Union[pandas.Series, pandas.DataFrame]#
 
                  The target variable(s) for all observations.
                  
 
                  Represented as a series if there is only a single target, or as a data frame if
 there are multiple targets.
                  
 
                  
 
                  Return type
                  
-
                  Union[Series, DataFrame]
                  
+
                  Union[Series, DataFrame]
                  
 
                  
 
                  
 
                  
@@ -816,12 +816,12 @@ 
                  facet.data.Sample
 
                  
-property weight: Optional[pandas.Series]#
                  
+property weight: Optional[pandas.Series]#
 
                  A series indicating the weight for each observation; None if no weights
 are defined.
                  
 
                  
 
                  Return type
                  
-
                  Optional[Series]
                  
+
                  Optional[Series]
                  
 
                  
 
                  
 
                  
diff --git a/docs/apidoc/facet/data/partition/facet.data.partition.CategoryPartitioner.html b/docs/apidoc/facet/data/partition/facet.data.partition.CategoryPartitioner.html
index a61f81fc..5c911dce 100644
--- a/docs/apidoc/facet/data/partition/facet.data.partition.CategoryPartitioner.html
+++ b/docs/apidoc/facet/data/partition/facet.data.partition.CategoryPartitioner.html
@@ -657,7 +657,7 @@ 
                  facet.data.partition.CategoryPartitioner
 
                  Parameters
                  
 
                    
-
                  • values (ndarray[Any, dtype[Any]]) – a sequence of observed values as the empirical basis for
+
                  • values (ndarray[Any, dtype[Any]]) – a sequence of observed values as the empirical basis for
 calculating the partitions
                    • 
 
                  • fit_params (Any) – optional fitting parameters
                    • 
 
                  
@@ -673,11 +673,11 @@ 
                  facet.data.partition.CategoryPartitioner
 
                  
-property frequencies_: numpy.ndarray[Any, numpy.dtype[numpy.int64]]#
                  
+property frequencies_: numpy.ndarray[Any, numpy.dtype[numpy.int64]]#
 
                  The count of values allocated to each partition.
                  
 
                  
 
                  Return type
                  
-
                  ndarray[Any, dtype[int64]]
                  
+
                  ndarray[Any, dtype[int64]]
                  
 
                  
 
                  
 
                  
@@ -721,7 +721,7 @@ 
                  facet.data.partition.CategoryPartitioner
 
                  Return type
                  
-
                  Sequence[TypeVar(T_Values, bound= generic)]
                  
+
                  Sequence[TypeVar(T_Values, bound= generic)]
                  
 
                  
 
 
                  
diff --git a/docs/apidoc/facet/data/partition/facet.data.partition.ContinuousRangePartitioner.html b/docs/apidoc/facet/data/partition/facet.data.partition.ContinuousRangePartitioner.html
index bbb2e4f4..dc9f58ed 100644
--- a/docs/apidoc/facet/data/partition/facet.data.partition.ContinuousRangePartitioner.html
+++ b/docs/apidoc/facet/data/partition/facet.data.partition.ContinuousRangePartitioner.html
@@ -682,7 +682,7 @@ 
                  facet.data.partition.ContinuousRangePartitioner
 
                  Parameters
                  
 
                  
diff --git a/docs/apidoc/facet/data/partition/facet.data.partition.IntegerRangePartitioner.html b/docs/apidoc/facet/data/partition/facet.data.partition.IntegerRangePartitioner.html
index b7788e90..f0e1af6f 100644
--- a/docs/apidoc/facet/data/partition/facet.data.partition.IntegerRangePartitioner.html
+++ b/docs/apidoc/facet/data/partition/facet.data.partition.IntegerRangePartitioner.html
@@ -682,7 +682,7 @@ 
                  facet.data.partition.IntegerRangePartitioner
 
                  Parameters
                  
 
                  
diff --git a/docs/apidoc/facet/data/partition/facet.data.partition.Partitioner.html b/docs/apidoc/facet/data/partition/facet.data.partition.Partitioner.html
index 286a71e2..01bd47a7 100644
--- a/docs/apidoc/facet/data/partition/facet.data.partition.Partitioner.html
+++ b/docs/apidoc/facet/data/partition/facet.data.partition.Partitioner.html
@@ -598,10 +598,10 @@ 
                  facet.data.partition.Partitioner
 
                  Bases
                  
-
                  FittableMixin [ndarray [Any, dtype [~T_Values]]]
                  
+
                  FittableMixin [ndarray [Any, dtype [~T_Values]]]
                  
 
                  
 
                  Generic types
                  
-
                  ~T_Values(bound= generic)
                  
+
                  ~T_Values(bound= generic)
                  
 
                  
 
                  Metaclasses
                  
 
                  ABCMeta
                  
@@ -640,7 +640,7 @@ 
                  facet.data.partition.PartitionerTrue if this is partitioner handles categorical values, False otherwise.
                  
 
 
                  is_fitted
                  
-
                  See pytools.fit.FittableMixin.is_fitted
                  
+
                  See pytools.fit.FittableMixin.is_fitted
                  
 
 
                  max_partitions
                  
 
                  The maximum number of partitions to be generated by this partitioner.
                  
@@ -658,7 +658,7 @@ 
                  facet.data.partition.Partitioner
 
                  Parameters
                  
 
                  
@@ -674,11 +674,11 @@ 
                  facet.data.partition.Partitioner
 
                  
-property frequencies_: numpy.ndarray[Any, numpy.dtype[numpy.int64]]#
                  
+property frequencies_: numpy.ndarray[Any, numpy.dtype[numpy.int64]]#
 
                  The count of values allocated to each partition.
                  
 
                  
 
                  Return type
                  
-
                  ndarray[Any, dtype[int64]]
                  
+
                  ndarray[Any, dtype[int64]]
                  
 
                  
 
                  
 
                  
@@ -697,7 +697,7 @@ 
                  facet.data.partition.Partitioner
 
                  
 abstract property is_fitted: bool#
                  
-
                  See pytools.fit.FittableMixin.is_fitted
                  
+
                  See pytools.fit.FittableMixin.is_fitted
                  
 
                  
 
 
                  
@@ -717,7 +717,7 @@ 
                  facet.data.partition.Partitioner
 
                  Return type
                  
-
                  Sequence[TypeVar(T_Values, bound= generic)]
                  
+
                  Sequence[TypeVar(T_Values, bound= generic)]
                  
 
                  
 
                  
 
                  
diff --git a/docs/apidoc/facet/data/partition/facet.data.partition.RangePartitioner.html b/docs/apidoc/facet/data/partition/facet.data.partition.RangePartitioner.html
index 60cbe794..44172036 100644
--- a/docs/apidoc/facet/data/partition/facet.data.partition.RangePartitioner.html
+++ b/docs/apidoc/facet/data/partition/facet.data.partition.RangePartitioner.html
@@ -672,7 +672,7 @@ 
                  facet.data.partition.RangePartitioner
 
                  Parameters
                  
 
                  
diff --git a/docs/apidoc/facet/explanation/base/facet.explanation.base.BaseExplainer.html b/docs/apidoc/facet/explanation/base/facet.explanation.base.BaseExplainer.html
index f1c7641a..a65421fd 100644
--- a/docs/apidoc/facet/explanation/base/facet.explanation.base.BaseExplainer.html
+++ b/docs/apidoc/facet/explanation/base/facet.explanation.base.BaseExplainer.html
@@ -613,7 +613,7 @@ 
                  facet.explanation.base.BaseExplainershap 0.36, the old API is deprecated for the majority of explainers
-while the shap.KernelExplainer still uses the old API exclusively
+while the shap.KernelExplainer still uses the old API exclusively
 in shap 0.41.
 We remedy this by adding support for both APIs to all explainers created through
 an ExplainerFactory object.
                  
@@ -702,15 +702,15 @@ 
                  facet.explanation.base.BaseExplainer
 
                  Parameters
                  
 
                  
 
                  
 
                  Return type
                  
-
                  Union[ndarray[Any, dtype[float64]], List[ndarray[Any, dtype[float64]]]]
                  
+
                  Union[ndarray[Any, dtype[float64]], List[ndarray[Any, dtype[float64]]]]
                  
 
                  
 
                  Returns
                  
 
                  SHAP values as an array of shape
@@ -727,15 +727,15 @@ 
                  facet.explanation.base.BaseExplainer
 
                  Parameters
                  
 
                  
 
                  
 
                  Return type
                  
-
                  Union[ndarray[Any, dtype[float64]], List[ndarray[Any, dtype[float64]]]]
                  
+
                  Union[ndarray[Any, dtype[float64]], List[ndarray[Any, dtype[float64]]]]
                  
 
                  
 
                  Returns
                  
 
                  SHAP values as an array of shape (n_observations, n_features);
diff --git a/docs/apidoc/facet/explanation/base/facet.explanation.base.ExplainerFactory.html b/docs/apidoc/facet/explanation/base/facet.explanation.base.ExplainerFactory.html
index 5dcc61f2..87c3c612 100644
--- a/docs/apidoc/facet/explanation/base/facet.explanation.base.ExplainerFactory.html
+++ b/docs/apidoc/facet/explanation/base/facet.explanation.base.ExplainerFactory.html
@@ -598,7 +598,7 @@ 
                  facet.explanation.base.ExplainerFactoryExplainer objects.
                  
 
                  
 
                  Bases
                  
-
                  HasExpressionRepr
                  
+
                  HasExpressionRepr
                  
 
                  
 
                  Generic types
                  
 
                  ~T_Model
                  
@@ -622,7 +622,7 @@ 
                  facet.explanation.base.ExplainerFactoryExplainer to compute shap values.
                  
 
 
                  to_expression
                  
-
                  See pytools.expression.HasExpressionRepr.to_expression()
                  
+
                  See pytools.expression.HasExpressionRepr.to_expression()
                  
 
 
 
@@ -656,7 +656,7 @@ 
                  facet.explanation.base.ExplainerFactoryParameters
 
                  
 
                  
 
                  Return type
                  
@@ -671,7 +671,7 @@ 
                  facet.explanation.base.ExplainerFactory
 
                  
 abstract to_expression()#
                  
-
                  See pytools.expression.HasExpressionRepr.to_expression()
                  
+
                  See pytools.expression.HasExpressionRepr.to_expression()
                  
 
                  
 
 
                  
diff --git a/docs/apidoc/facet/explanation/facet.explanation.ExactExplainerFactory.html b/docs/apidoc/facet/explanation/facet.explanation.ExactExplainerFactory.html
index c096f381..0d0dc960 100644
--- a/docs/apidoc/facet/explanation/facet.explanation.ExactExplainerFactory.html
+++ b/docs/apidoc/facet/explanation/facet.explanation.ExactExplainerFactory.html
@@ -655,8 +655,8 @@ 
                  facet.explanation.ExactExplainerFactory
 
                  Parameters
                  
 
                  
 
                  
 
                  Return type
                  
@@ -675,8 +675,8 @@ 
                  facet.explanation.ExactExplainerFactory
 
                  Parameters
                  
 
                  
 
                  
 
                  Return type
                  
@@ -694,7 +694,7 @@ 
                  facet.explanation.ExactExplainerFactory
 
                  Return type
                  
-
                  Expression
                  
+
                  Expression
                  
 
                  
 
                  Returns
                  
 
                  the expression representing this object
                  
diff --git a/docs/apidoc/facet/explanation/facet.explanation.FunctionExplainerFactory.html b/docs/apidoc/facet/explanation/facet.explanation.FunctionExplainerFactory.html
index ec3b9ef2..2463300a 100644
--- a/docs/apidoc/facet/explanation/facet.explanation.FunctionExplainerFactory.html
+++ b/docs/apidoc/facet/explanation/facet.explanation.FunctionExplainerFactory.html
@@ -599,7 +599,7 @@ 
                  facet.explanation.FunctionExplainerFactory
 
                  Bases
                  
-
                  ExplainerFactory [Union [RegressorMixin, ClassifierMixin, Callable [[typing.Union[pandas.core.series.Series, pandas.core.frame.DataFrame, numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]]], Union [Series, ndarray [Any, dtype [float64]], float]]]]
                  
+
                  ExplainerFactory [Union [RegressorMixin, ClassifierMixin, Callable [[typing.Union[pandas.core.series.Series, pandas.core.frame.DataFrame, numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]]], Union [Series, ndarray [Any, dtype [float64]], float]]]]
                  
 
                  
 
                  Metaclasses
                  
 
                  ABCMeta
                  
@@ -623,7 +623,7 @@ 
                  facet.explanation.FunctionExplainerFactory
                  to_expression
                  
-
                  See pytools.expression.HasExpressionRepr.to_expression()
                  
+
                  See pytools.expression.HasExpressionRepr.to_expression()
                  
 
 
 
@@ -656,8 +656,8 @@ 
                  facet.explanation.FunctionExplainerFactory
 
                  Parameters
                  
 
                  
 
                  
 
                  Return type
                  
@@ -676,8 +676,8 @@ 
                  facet.explanation.FunctionExplainerFactory
 
                  Parameters
                  
 
                  
 
                  
 
                  Return type
                  
@@ -692,7 +692,7 @@ 
                  facet.explanation.FunctionExplainerFactory
 
                  
 abstract to_expression()#
                  
-
                  See pytools.expression.HasExpressionRepr.to_expression()
                  
+
                  See pytools.expression.HasExpressionRepr.to_expression()
                  
 
                  
 
 
                  
diff --git a/docs/apidoc/facet/explanation/facet.explanation.KernelExplainerFactory.html b/docs/apidoc/facet/explanation/facet.explanation.KernelExplainerFactory.html
index 644ecf81..0c0a3b16 100644
--- a/docs/apidoc/facet/explanation/facet.explanation.KernelExplainerFactory.html
+++ b/docs/apidoc/facet/explanation/facet.explanation.KernelExplainerFactory.html
@@ -595,7 +595,7 @@ 
                  facet.explanation.KernelExplainerFactory
 
                  
 class facet.explanation.KernelExplainerFactory(*, link=None, l1_reg='num_features(10)', data_size_limit=100, **explainer_kwargs)[source]#
                  
-
                  A factory constructing KernelExplainer instances.
                  
+
                  A factory constructing KernelExplainer instances.
                  
 
                  
 
                  Bases
                  
 
                  FunctionExplainerFactory
                  
@@ -607,8 +607,8 @@ 
                  facet.explanation.KernelExplainerFactory
                  
 
diff --git a/docs/apidoc/facet/explanation/parallel/facet.explanation.parallel.ExplainerQueue.html b/docs/apidoc/facet/explanation/parallel/facet.explanation.parallel.ExplainerQueue.html
index dacc1369..41f4ab7d 100644
--- a/docs/apidoc/facet/explanation/parallel/facet.explanation.parallel.ExplainerQueue.html
+++ b/docs/apidoc/facet/explanation/parallel/facet.explanation.parallel.ExplainerQueue.html
@@ -598,7 +598,7 @@ 
                  facet.explanation.parallel.ExplainerQueue
 
                  Bases
                  
-
                  JobQueue [Union [ndarray [Any, dtype [Any]], List [ndarray [Any, dtype [Any]]]], Union [ndarray [Any, dtype [Any]], List [ndarray [Any, dtype [Any]]]]]
                  
+
                  JobQueue [Union [ndarray [Any, dtype [Any]], List [ndarray [Any, dtype [Any]]]], Union [ndarray [Any, dtype [Any]], List [ndarray [Any, dtype [Any]]]]]
                  
 
                  
 
                  Metaclasses
                  
 
                  ABCMeta
                  
@@ -606,8 +606,8 @@ 
                  facet.explanation.parallel.ExplainerQueueParameters
 
                  
 
 
                  
 
                  
-X: numpy.ndarray[Any, numpy.dtype[Any]]#
                  
+X: numpy.ndarray[Any, numpy.dtype[Any]]#
 
                  the feature values of the observations to be explained
                  
 
                  
 
@@ -739,7 +739,7 @@ 
                  facet.explanation.parallel.ExplainerQueue
 
                  
-y: Optional[numpy.ndarray[Any, numpy.dtype[Any]]]#
                  
+y: Optional[numpy.ndarray[Any, numpy.dtype[Any]]]#
 
                  the target values of the observations to be explained
                  
 
                  
 
diff --git a/docs/apidoc/facet/explanation/parallel/facet.explanation.parallel.ParallelExplainer.html b/docs/apidoc/facet/explanation/parallel/facet.explanation.parallel.ParallelExplainer.html
index 5027b086..e17a1b67 100644
--- a/docs/apidoc/facet/explanation/parallel/facet.explanation.parallel.ParallelExplainer.html
+++ b/docs/apidoc/facet/explanation/parallel/facet.explanation.parallel.ParallelExplainer.html
@@ -601,7 +601,7 @@ 
                  facet.explanation.parallel.ParallelExplainer
 
                  Bases
                  
-
                  ParallelizableMixin, BaseExplainer
                  
+
                  ParallelizableMixin, BaseExplainer
                  
 
                  
 
                  Metaclasses
                  
 
                  ABCMeta
                  
@@ -725,15 +725,15 @@ 
                  facet.explanation.parallel.ParallelExplainer
 
                  Parameters
                  
 
                  
 
                  
 
                  Return type
                  
-
                  Union[ndarray[Any, dtype[float64]], List[ndarray[Any, dtype[float64]]]]
                  
+
                  Union[ndarray[Any, dtype[float64]], List[ndarray[Any, dtype[float64]]]]
                  
 
                  
 
                  Returns
                  
 
                  SHAP values as an array of shape
@@ -750,15 +750,15 @@ 
                  facet.explanation.parallel.ParallelExplainer
 
                  Parameters
                  
 
                  
 
                  
 
                  Return type
                  
-
                  Union[ndarray[Any, dtype[float64]], List[ndarray[Any, dtype[float64]]]]
                  
+
                  Union[ndarray[Any, dtype[float64]], List[ndarray[Any, dtype[float64]]]]
                  
 
                  
 
                  Returns
                  
 
                  SHAP values as an array of shape (n_observations, n_features);
diff --git a/docs/apidoc/facet/facet.explanation.html b/docs/apidoc/facet/facet.explanation.html
index 0a47e6a0..79f6ac72 100644
--- a/docs/apidoc/facet/facet.explanation.html
+++ b/docs/apidoc/facet/facet.explanation.html
@@ -626,13 +626,13 @@ 
                  ClassesExplainer instances that use Python functions as the underlying model.
                  
 
 
                  KernelExplainerFactory
                  
-
                  A factory constructing KernelExplainer instances.
                  
+
                  A factory constructing KernelExplainer instances.
                  
 
 
                  PermutationExplainerFactory
                  
 
                  A factory constructing Permutation explainer instances.
                  
 
 
                  TreeExplainerFactory
                  
-
                  A factory constructing TreeExplainer instances.
                  
+
                  A factory constructing TreeExplainer instances.
                  
 
 
 
diff --git a/docs/apidoc/facet/facet.inspection.html b/docs/apidoc/facet/facet.inspection.html
index 9165aa8f..d50993a0 100644
--- a/docs/apidoc/facet/facet.inspection.html
+++ b/docs/apidoc/facet/facet.inspection.html
@@ -623,7 +623,7 @@ 
                  Classes
                  LearnerInspector
                  
-
                  Explain an sklearndf regressor or classifier based on SHAP values.
                  
+
                  Explain an sklearndf regressor or classifier based on SHAP values.
                  
 
 
                  NativeLearnerInspector
                  
 
                  Explain a native scikit-learn regressor or classifier based on SHAP values.
                  
diff --git a/docs/apidoc/facet/inspection/base/facet.inspection.base.ModelInspector.html b/docs/apidoc/facet/inspection/base/facet.inspection.base.ModelInspector.html
index 2e65bda3..7faa9dca 100644
--- a/docs/apidoc/facet/inspection/base/facet.inspection.base.ModelInspector.html
+++ b/docs/apidoc/facet/inspection/base/facet.inspection.base.ModelInspector.html
@@ -630,7 +630,7 @@ 
                  facet.inspection.base.ModelInspector
 
                  Bases
                  
-
                  ParallelizableMixin, FittableMixin [Sample]
                  
+
                  FittableMixin [Sample], ParallelizableMixin
                  
 
                  
 
                  Generic types
                  
 
                  ~T_Model
                  
@@ -761,7 +761,7 @@ 
                  facet.inspection.base.ModelInspector
 
                  Return type
                  
-
                  Union[LinkageTree, List[LinkageTree]]
                  
+
                  Union[LinkageTree, List[LinkageTree]]
                  
 
                  
 
                  Returns
                  
 
                  linkage tree of feature associations; list of linkage trees
@@ -803,7 +803,7 @@ 
                  facet.inspection.base.ModelInspectorReturn type
-
                  Union[Matrix[float64], List[Matrix[float64]]]
                  
+
                  Union[Matrix[float64], List[Matrix[float64]]]
                  
 
                  
 
                  Returns
                  
 
                  feature association matrix as a data frame of shape
@@ -826,7 +826,7 @@ 
                  facet.inspection.base.ModelInspectorReturn type
-
                  Union[Series, DataFrame]
                  
+
                  Union[Series, DataFrame]
                  
 
                  
 
                  Returns
                  
 
                  a series of length n_features for single-output models, or a
@@ -872,7 +872,7 @@ 
                  facet.inspection.base.ModelInspector
 
                  Return type
                  
-
                  Union[Matrix[float64], List[Matrix[float64]]]
                  
+
                  Union[Matrix[float64], List[Matrix[float64]]]
                  
 
                  
 
                  Returns
                  
 
                  relative shap interaction values as a data frame of shape
@@ -891,7 +891,7 @@ 
                  facet.inspection.base.ModelInspector
 
                  Return type
                  
-
                  Union[LinkageTree, List[LinkageTree]]
                  
+
                  Union[LinkageTree, List[LinkageTree]]
                  
 
                  
 
                  Returns
                  
 
                  linkage tree of feature redundancies; list of linkage trees
@@ -932,7 +932,7 @@ 
                  facet.inspection.base.ModelInspectorReturn type
-
                  Union[Matrix[float64], List[Matrix[float64]]]
                  
+
                  Union[Matrix[float64], List[Matrix[float64]]]
                  
 
                  
 
                  Returns
                  
 
                  feature redundancy matrix as a data frame of shape
@@ -951,7 +951,7 @@ 
                  facet.inspection.base.ModelInspector
 
                  Return type
                  
-
                  Union[LinkageTree, List[LinkageTree]]
                  
+
                  Union[LinkageTree, List[LinkageTree]]
                  
 
                  
 
                  Returns
                  
 
                  linkage tree of feature synergies; list of linkage trees
@@ -992,7 +992,7 @@ 
                  facet.inspection.base.ModelInspectorReturn type
-
                  Union[Matrix[float64], List[Matrix[float64]]]
                  
+
                  Union[Matrix[float64], List[Matrix[float64]]]
                  
 
                  
 
                  Returns
                  
 
                  feature synergy matrix as a data frame of shape
@@ -1034,10 +1034,10 @@ 
                  facet.inspection.base.ModelInspector
 
                  Parameters
                  
-
                  features (Union[DataFrame, Series]) – features to preprocess
                  
+
                  features (Union[DataFrame, Series]) – features to preprocess
                  
 
                  
 
                  Return type
                  
-
                  DataFrame
                  
+
                  DataFrame
                  
 
                  
 
                  Returns
                  
 
                  preprocessed features
                  
@@ -1055,7 +1055,7 @@ 
                  facet.inspection.base.ModelInspector
 
                  Return type
                  
-
                  Union[DataFrame, List[DataFrame]]
                  
+
                  Union[DataFrame, List[DataFrame]]
                  
 
                  
 
                  Returns
                  
 
                  a data frame with SHAP interaction values
                  
@@ -1107,7 +1107,7 @@ 
                  facet.inspection.base.ModelInspector
 
                  Return type
                  
-
                  Union[DataFrame, List[DataFrame]]
                  
+
                  Union[DataFrame, List[DataFrame]]
                  
 
                  
 
                  Returns
                  
 
                  a data frame with SHAP values
                  
diff --git a/docs/apidoc/facet/inspection/facet.inspection.FunctionInspector.html b/docs/apidoc/facet/inspection/facet.inspection.FunctionInspector.html
index 40f9cd31..386f3689 100644
--- a/docs/apidoc/facet/inspection/facet.inspection.FunctionInspector.html
+++ b/docs/apidoc/facet/inspection/facet.inspection.FunctionInspector.html
@@ -628,7 +628,7 @@ 
                  facet.inspection.FunctionInspector
                  ModelInspector [~T_Function]
                  
 
                  
 
                  Generic types
                  
-
                  ~T_Function(bound= Callable [[typing.Union[pandas.core.series.Series, pandas.core.frame.DataFrame, numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]]], Union [Series, ndarray [Any, dtype [float64]], float]])
                  
+
                  ~T_Function(bound= Callable [[typing.Union[pandas.core.series.Series, pandas.core.frame.DataFrame, numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]]], Union [Series, ndarray [Any, dtype [float64]], float]])
                  
 
                  
 
                  Metaclasses
                  
 
                  ABCMeta
                  
@@ -767,7 +767,7 @@ 
                  facet.inspection.FunctionInspector
 
                  Return type
                  
-
                  Union[LinkageTree, List[LinkageTree]]
                  
+
                  Union[LinkageTree, List[LinkageTree]]
                  
 
                  
 
                  Returns
                  
 
                  linkage tree of feature associations; list of linkage trees
@@ -809,7 +809,7 @@ 
                  facet.inspection.FunctionInspectorReturn type
-
                  Union[Matrix[float64], List[Matrix[float64]]]
                  
+
                  Union[Matrix[float64], List[Matrix[float64]]]
                  
 
                  
 
                  Returns
                  
 
                  feature association matrix as a data frame of shape
@@ -832,7 +832,7 @@ 
                  facet.inspection.FunctionInspectorReturn type
-
                  Union[Series, DataFrame]
                  
+
                  Union[Series, DataFrame]
                  
 
                  
 
                  Returns
                  
 
                  a series of length n_features for single-output models, or a
@@ -878,7 +878,7 @@ 
                  facet.inspection.FunctionInspector
 
                  Return type
                  
-
                  Union[Matrix[float64], List[Matrix[float64]]]
                  
+
                  Union[Matrix[float64], List[Matrix[float64]]]
                  
 
                  
 
                  Returns
                  
 
                  relative shap interaction values as a data frame of shape
@@ -897,7 +897,7 @@ 
                  facet.inspection.FunctionInspector
 
                  Return type
                  
-
                  Union[LinkageTree, List[LinkageTree]]
                  
+
                  Union[LinkageTree, List[LinkageTree]]
                  
 
                  
 
                  Returns
                  
 
                  linkage tree of feature redundancies; list of linkage trees
@@ -938,7 +938,7 @@ 
                  facet.inspection.FunctionInspectorReturn type
-
                  Union[Matrix[float64], List[Matrix[float64]]]
                  
+
                  Union[Matrix[float64], List[Matrix[float64]]]
                  
 
                  
 
                  Returns
                  
 
                  feature redundancy matrix as a data frame of shape
@@ -957,7 +957,7 @@ 
                  facet.inspection.FunctionInspector
 
                  Return type
                  
-
                  Union[LinkageTree, List[LinkageTree]]
                  
+
                  Union[LinkageTree, List[LinkageTree]]
                  
 
                  
 
                  Returns
                  
 
                  linkage tree of feature synergies; list of linkage trees
@@ -998,7 +998,7 @@ 
                  facet.inspection.FunctionInspectorReturn type
-
                  Union[Matrix[float64], List[Matrix[float64]]]
                  
+
                  Union[Matrix[float64], List[Matrix[float64]]]
                  
 
                  
 
                  Returns
                  
 
                  feature synergy matrix as a data frame of shape
@@ -1040,10 +1040,10 @@ 
                  facet.inspection.FunctionInspector
 
                  Parameters
                  
-
                  features (Union[DataFrame, Series]) – features to preprocess
                  
+
                  features (Union[DataFrame, Series]) – features to preprocess
                  
 
                  
 
                  Return type
                  
-
                  DataFrame
                  
+
                  DataFrame
                  
 
                  
 
                  Returns
                  
 
                  preprocessed features
                  
@@ -1061,7 +1061,7 @@ 
                  facet.inspection.FunctionInspector
 
                  Return type
                  
-
                  Union[DataFrame, List[DataFrame]]
                  
+
                  Union[DataFrame, List[DataFrame]]
                  
 
                  
 
                  Returns
                  
 
                  a data frame with SHAP interaction values
                  
@@ -1113,7 +1113,7 @@ 
                  facet.inspection.FunctionInspector
 
                  Return type
                  
-
                  Union[DataFrame, List[DataFrame]]
                  
+
                  Union[DataFrame, List[DataFrame]]
                  
 
                  
 
                  Returns
                  
 
                  a data frame with SHAP values
                  
diff --git a/docs/apidoc/facet/inspection/facet.inspection.LearnerInspector.html b/docs/apidoc/facet/inspection/facet.inspection.LearnerInspector.html
index b3f800f3..783c7c27 100644
--- a/docs/apidoc/facet/inspection/facet.inspection.LearnerInspector.html
+++ b/docs/apidoc/facet/inspection/facet.inspection.LearnerInspector.html
@@ -597,7 +597,7 @@ 
                  facet.inspection.LearnerInspector
 
                  
 class facet.inspection.LearnerInspector(model, *, explainer_factory=None, shap_interaction=True, n_jobs=None, shared_memory=None, pre_dispatch=None, verbose=None)[source]#
                  
-
                  Explain an sklearndf regressor or classifier based on SHAP values.
                  
+
                  Explain an sklearndf regressor or classifier based on SHAP values.
                  
 
                  Focus is on explaining the overall model, but the inspector also delivers
 SHAP explanations of the individual observations.
                  
 
                  Available inspection methods are:
                  
@@ -628,7 +628,7 @@ 
                  facet.inspection.LearnerInspector
                  ModelInspector [~T_SupervisedLearner]
                  
 
                  
 
                  Generic types
                  
-
                  ~T_SupervisedLearnerDF(bound= SupervisedLearnerDF)
                  
+
                  ~T_SupervisedLearnerDF(bound= SupervisedLearnerDF)
                  
 
                  
 
                  Metaclasses
                  
 
                  ABCMeta
                  
@@ -636,8 +636,8 @@ 
                  facet.inspection.LearnerInspectorParameters
 
                  
 
                  
@@ -641,9 +641,9 @@ 
                  facet.validation.StationaryBootstrapCV
 
                  Parameters
                  
 
                  
 
                  
 
                  Return type
                  
@@ -662,14 +662,14 @@ 
                  facet.validation.StationaryBootstrapCV
 
                  Parameters
                  
 
                  
 
                  
 
                  Return type
                  
-
                  Generator[Tuple[ndarray[Any, dtype[int64]], ndarray[Any, dtype[int64]]], None, None]
                  
+
                  Generator[Tuple[ndarray[Any, dtype[int64]], ndarray[Any, dtype[int64]]], None, None]
                  
 
                  
 
                  Returns
                  
 
                  a generator yielding (train, test) tuples where
diff --git a/docs/apidoc/facet/validation/facet.validation.StratifiedBootstrapCV.html b/docs/apidoc/facet/validation/facet.validation.StratifiedBootstrapCV.html
index e75b2aed..30794714 100644
--- a/docs/apidoc/facet/validation/facet.validation.StratifiedBootstrapCV.html
+++ b/docs/apidoc/facet/validation/facet.validation.StratifiedBootstrapCV.html
@@ -612,7 +612,7 @@ 
                  facet.validation.StratifiedBootstrapCVParameters
 
                  
 
                  
@@ -640,9 +640,9 @@ 
                  facet.validation.StratifiedBootstrapCV
 
                  Parameters
                  
 
                  
 
                  
 
                  Return type
                  
@@ -661,14 +661,14 @@ 
                  facet.validation.StratifiedBootstrapCV
 
                  Parameters
                  
 
                  
 
                  
 
                  Return type
                  
-
                  Generator[Tuple[ndarray[Any, dtype[int64]], ndarray[Any, dtype[int64]]], None, None]
                  
+
                  Generator[Tuple[ndarray[Any, dtype[int64]], ndarray[Any, dtype[int64]]], None, None]
                  
 
                  
 
                  Returns
                  
 
                  a generator yielding (train, test) tuples where
diff --git a/docs/contribution_guide.html b/docs/contribution_guide.html
index 2a372856..46782603 100644
--- a/docs/contribution_guide.html
+++ b/docs/contribution_guide.html
@@ -519,7 +519,7 @@ 
                  Sphinx Buildmake.py and conf.py. The base configuration for the
-these scripts can be found in pytools/sphinx.
+these scripts can be found in pytools/sphinx.
 The reason for this is to minimise code given the standardization of the documentation
 build across multiple packages.
                  
 
                  make.py: All base configuration comes from pytools/sphinx/base/make_base.py and
@@ -656,7 +656,7 @@ 
                  Package builds
 
                • make.py: generic Python script for package builds. Most configuration is imported
-from pytools make.py
+from pytools make.py
 which is a build script that wraps the package build, as well as exposing the matrix
 dependency definitions specified in the pyproject.toml as environment variables
                  • 
 
                • pyproject.toml: metadata for PyPI, build settings and package dependencies
                  • 
diff --git a/docs/docs-version/1-0/_static/js/versions.js b/docs/docs-version/1-0/_static/js/versions.js
index 2178ec61..c87743e6 100644
--- a/docs/docs-version/1-0/_static/js/versions.js
+++ b/docs/docs-version/1-0/_static/js/versions.js
@@ -1,7 +1,7 @@
 const DOCS_VERSIONS = {
-    "current": "2.1.0",
+    "current": "2.1.1",
     "all": [
-        "2.1.0",
+        "2.1.1",
         "2.0.1",
         "1.2.3",
         "1.1.2",
diff --git a/docs/docs-version/1-1/_static/js/versions.js b/docs/docs-version/1-1/_static/js/versions.js
index 2178ec61..c87743e6 100644
--- a/docs/docs-version/1-1/_static/js/versions.js
+++ b/docs/docs-version/1-1/_static/js/versions.js
@@ -1,7 +1,7 @@
 const DOCS_VERSIONS = {
-    "current": "2.1.0",
+    "current": "2.1.1",
     "all": [
-        "2.1.0",
+        "2.1.1",
         "2.0.1",
         "1.2.3",
         "1.1.2",
diff --git a/docs/docs-version/1-2/_static/js/versions.js b/docs/docs-version/1-2/_static/js/versions.js
index 2178ec61..c87743e6 100644
--- a/docs/docs-version/1-2/_static/js/versions.js
+++ b/docs/docs-version/1-2/_static/js/versions.js
@@ -1,7 +1,7 @@
 const DOCS_VERSIONS = {
-    "current": "2.1.0",
+    "current": "2.1.1",
     "all": [
-        "2.1.0",
+        "2.1.1",
         "2.0.1",
         "1.2.3",
         "1.1.2",
diff --git a/docs/docs-version/2-0/_static/js/versions.js b/docs/docs-version/2-0/_static/js/versions.js
index 2178ec61..c87743e6 100644
--- a/docs/docs-version/2-0/_static/js/versions.js
+++ b/docs/docs-version/2-0/_static/js/versions.js
@@ -1,7 +1,7 @@
 const DOCS_VERSIONS = {
-    "current": "2.1.0",
+    "current": "2.1.1",
     "all": [
-        "2.1.0",
+        "2.1.1",
         "2.0.1",
         "1.2.3",
         "1.1.2",
diff --git a/docs/docs-version/2-1/_downloads/87bd7a5fc5edee5f7e46545ac3ff5913/Water_Drilling_Incident_Classification_with_Facet.ipynb b/docs/docs-version/2-1/_downloads/87bd7a5fc5edee5f7e46545ac3ff5913/Water_Drilling_Incident_Classification_with_Facet.ipynb
index 6289d3e0..5a02f025 100644
--- a/docs/docs-version/2-1/_downloads/87bd7a5fc5edee5f7e46545ac3ff5913/Water_Drilling_Incident_Classification_with_Facet.ipynb
+++ b/docs/docs-version/2-1/_downloads/87bd7a5fc5edee5f7e46545ac3ff5913/Water_Drilling_Incident_Classification_with_Facet.ipynb
@@ -29,7 +29,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.       \n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.       \n",
     "\n",
     "***\n",
     "\n",
@@ -111,7 +111,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, etc.)\n",
     "2. Required FACET classes (inspection, selection, validation, simulation, etc.)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-Gamma/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-Gamma/pytools)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-X-Official/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-X-Official/pytools)) when using FACET"
    ]
   },
   {
@@ -191,7 +191,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
+    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
@@ -231,7 +231,7 @@
    "source": [
     "**pytools imports**\n",
     "\n",
-    "pytools (see on [GitHub](https://github.com/BCG-Gamma/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
+    "pytools (see on [GitHub](https://github.com/BCG-X-Official/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
    ]
   },
   {
diff --git a/docs/docs-version/2-1/_downloads/cf9a77d79c2a3565a488450ce979e06f/Scikit-learn_classifier_summaries_using_FACET.ipynb b/docs/docs-version/2-1/_downloads/cf9a77d79c2a3565a488450ce979e06f/Scikit-learn_classifier_summaries_using_FACET.ipynb
index 00f821d3..02385b16 100644
--- a/docs/docs-version/2-1/_downloads/cf9a77d79c2a3565a488450ce979e06f/Scikit-learn_classifier_summaries_using_FACET.ipynb
+++ b/docs/docs-version/2-1/_downloads/cf9a77d79c2a3565a488450ce979e06f/Scikit-learn_classifier_summaries_using_FACET.ipynb
@@ -27,7 +27,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
     "\n",
     "***\n",
     "\n",
@@ -89,7 +89,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, sklearn, etc.)\n",
     "2. Required FACET classes (i.e., selection)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)) when using FACET"
    ]
   },
   {
@@ -151,7 +151,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
+    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
diff --git a/docs/docs-version/2-1/_downloads/f8edc62901c3352636cbd43e4d813713/Classification_with_Facet.ipynb b/docs/docs-version/2-1/_downloads/f8edc62901c3352636cbd43e4d813713/Classification_with_Facet.ipynb
index 633dd449..9a61e41e 100644
--- a/docs/docs-version/2-1/_downloads/f8edc62901c3352636cbd43e4d813713/Classification_with_Facet.ipynb
+++ b/docs/docs-version/2-1/_downloads/f8edc62901c3352636cbd43e4d813713/Classification_with_Facet.ipynb
@@ -31,7 +31,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
     "\n",
     "***\n",
     "\n",
@@ -108,7 +108,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, etc.)\n",
     "2. Required FACET classes (inspection, selection, validation, simulation, etc.)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-Gamma/pytools)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-X-Official/pytools)) when using FACET"
    ]
   },
   {
@@ -162,7 +162,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
+    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
@@ -194,7 +194,7 @@
    "source": [
     "**pytools imports**\n",
     "\n",
-    "pytools (see on [GitHub](https://github.com/BCG-Gamma/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
+    "pytools (see on [GitHub](https://github.com/BCG-X-Official/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
    ]
   },
   {
@@ -1557,7 +1557,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For convenience when working in a non-notebook environment, all of the `Drawer`'s provided by the [pytools](https://github.com/BCG-Gamma/pytools) package also support a `style='text'` flag."
+    "For convenience when working in a non-notebook environment, all of the `Drawer`'s provided by the [pytools](https://github.com/BCG-X-Official/pytools) package also support a `style='text'` flag."
    ]
   },
   {
diff --git a/docs/docs-version/2-1/_generated/getting_started.html b/docs/docs-version/2-1/_generated/getting_started.html
index 84d3ddb9..fe0380ac 100644
--- a/docs/docs-version/2-1/_generated/getting_started.html
+++ b/docs/docs-version/2-1/_generated/getting_started.html
@@ -261,72 +261,10 @@
                 
   
                  
 
                  Getting started with#
                  
-../_images/Gamma_Facet_Logo_RGB_LB.svg
                  
-
                  Note
                  
-
                  FACET 2.0 now available!
                  
-
                  FACET 2.0 brings numerous API enhancements and improvements, accelerates model
-inspection by up to a factor of 50 in many practical applications, introduces a new,
-more flexible and user-friendly API for hyperparameter tuning – with support for
-scikit-learn’s native hyperparameter searchers – and improves the styling of all
-visualizations.
                  
-
                  See the release notes
-for more details.
                  
-
                  
-
                  FACET is an open source library for human-explainable AI.
+../_images/Gamma_Facet_Logo_RGB_LB.svg
                  FACET is an open source library for human-explainable AI.
 It combines sophisticated model inspection and model-based simulation to enable better
 explanations of your supervised machine learning models.
                  
 
                  FACET is composed of the following key components:
                  
-
----
-
-
-
-
-
-
-
-
-
-
-
-
                  



                        
                  
-
                  inspect
                  
-
                  Model Inspection
                  
-
                  FACET introduces a new algorithm to quantify dependencies and
-interactions between features in ML models.
-This new tool for human-explainable AI adds a new, global
-perspective to the observation-level explanations provided by the
-popular SHAP approach.
-To learn more about FACET’s model inspection capabilities, see the
-getting started example below.
                  
-
                        
                  
-
                  sim
                  
-
                  Model Simulation
                  
-
                  FACET’s model simulation algorithms use ML models for
-virtual experiments to help identify scenarios that optimise
-predicted outcomes.
-To quantify the uncertainty in simulations, FACET utilises a range
-of bootstrapping algorithms including stationary and stratified
-bootstraps.
-For an example of FACET’s bootstrap simulations, see the
-quickstart example below.
                  
-
                        
                  
-
                  pipe
                  
-
                  Enhanced Machine Learning Workflow
                  
-
                  FACET offers an efficient and transparent machine learning
-workflow, enhancing
-scikit-learn’s
-tried and tested pipelining paradigm with new capabilities for model
-selection, inspection, and simulation.
-FACET also introduces
-sklearndf
-[documentation]
-an augmented version of scikit-learn with enhanced support for
-pandas data frames that ensures end-to-end traceability of features.
                  
-
                  
 
                  
 
                  Installation#
                  
 
                  FACET supports both PyPI and Anaconda.
@@ -364,9 +302,9 @@ 
                  QuickstartFACET documentation.
                  
+see the FACET documentation.
                  
 
                  Changes and additions to new versions are summarized in the
-release notes.
                  
+release notes.
                  
 
                  
 
                  Enhanced Machine Learning Workflow#
                  
 
                  To demonstrate the model inspection capability of FACET, we first create a
@@ -572,7 +510,7 @@ 
                  Model InspectionAPI reference
+API reference
 for more detail.
                  
 
                  
 
                  
@@ -630,17 +568,17 @@ 
                  ContributingGitHub form, and if you wish to do so,
+GitHub form, and if you wish to do so,
 please open a PR addressing the issue.
                  
 
                  We do ask that for any major changes please discuss these with us first via an issue or
 using our team email: FacetTeam@bcg.com.
                  
 
                  For further information on contributing please see our
-contribution guide.
                  
+contribution guide.
                  
 
                  
 
                  
 
                  License#
                  
 
                  FACET is licensed under Apache 2.0 as described in the
-LICENSE file.
                  
+LICENSE file.
                  
 
                  
 
                  
 
                  Acknowledgements#
                  
@@ -657,7 +595,7 @@ 
                  Acknowledgements
 
                  BCG GAMMA#
                  
 
                  If you would like to know more about the team behind FACET please see the
-about us page.
                  
+about us page.
                  
 
                  We are always on the lookout for passionate and talented data scientists to join the
 BCG GAMMA team. If you would like to know more you can find out about
 BCG GAMMA,
diff --git a/docs/docs-version/2-1/_generated/release_notes.html b/docs/docs-version/2-1/_generated/release_notes.html
index eeb25d2e..9d60c3d6 100644
--- a/docs/docs-version/2-1/_generated/release_notes.html
+++ b/docs/docs-version/2-1/_generated/release_notes.html
@@ -173,6 +173,11 @@
   
                    
    
                  • 
     
+     2.1.1
+    
+   
                    • 
+   
                  • 
+    
      2.1.0
     
    
                    • 
@@ -184,12 +189,12 @@
   
   
                      
    
                    • 
-    
+    
      2.0.1
     
    
                      • 
    
                    • 
-    
+    
      2.0.0
     
     
                        
@@ -262,22 +267,22 @@
   
   
                          
    
                        • 
-    
+    
      1.2.3
     
    
                          • 
    
                        • 
-    
+    
      1.2.2
     
    
                          • 
    
                        • 
-    
+    
      1.2.1
     
    
                          • 
    
                        • 
-    
+    
      1.2.0
     
    
                          • 
@@ -289,17 +294,17 @@
   
   
                            
    
                          • 
-    
+    
      1.1.2
     
    
                            • 
    
                          • 
-    
+    
      1.1.1
     
    
                            • 
    
                          • 
-    
+    
      1.1.0
     
    
                            • 
@@ -311,17 +316,17 @@
   
   
                              
    
                            • 
-    
+    
      1.0.3
     
    
                              • 
    
                            • 
-    
+    
      1.0.2
     
    
                              • 
    
                            • 
-    
+    
      1.0.1
     
    
                              • 
@@ -354,12 +359,16 @@
 
                              FACET 2.1#
                              
 
                              FACET 2.1 introduces the NativeLearnerInspector for inspecting native
 scikit-learn models and pipelines.
                              
-
                              We still recommend using sklearndf models and learner pipelines along with FACET’s
+
                              We still recommend using sklearndf models and learner pipelines along with FACET’s
 LearnerSelector for hyperparameter tuning; however the new
 NativeLearnerInspector can be useful for inspecting models that have been
 trained using scikit-learn directly.
                              
 
                              
-
                              2.1.0#
                              
+
                              2.1.1#
                              
+
                              This is a maintenance release to catch up with FACET 2.0.1.
                              
+
                              
+
                              
+
                              2.1.0#
                              
 
                                
 
                              • API: new NativeLearnerInspector class for inspecting native scikit-learn
 regressors, classifiers, and pipelines with a regressor or classifier as the final
@@ -374,19 +383,21 @@ 
                                FACET 2.0pytools 2.0 and sklearndf 2.2, and is now
-fully type-checked by mypy.
                                
-
                                
-
                                2.0.1#
                                
+
                                FACET 2.0 requires pytools 2.0 and sklearndf 2.2, and is now
+fully type-checked by mypy.
                                
+
                                
+
                                2.0.1#
                                
 
                                  
 
                                • API: class LearnerInspector now supports inspecting individual regressors
 and classifiers; it is no longer necessary to wrap them into a
 RegressorPipelineDF or ClassifierPipelineDF instance with empty
 preprocessing
                                  • 
+
                                • FIX: replace a call to method get_text_heights() of matplotlib.axes.Axes,
+which is deprecated as of matplotlib 3.6
                                  • 
 
                                
 
                                
-
                                
-
                                2.0.0#
                                
+
                                
+
                                2.0.0#
                                
 
                                
 
                                facet.data#
                                
 
                                  
@@ -416,17 +427,17 @@ 
                                  facet.inspectio
 
                                • API: LearnerInspector no longer uses learner crossfits and instead inspects
 models using a single pass of SHAP calculations, usually leading to performance gains
 of up to a factor of 50
                                  • 
-
                                • API: return LearnerInspector matrix outputs as Matrix
+
                                • API: return LearnerInspector matrix outputs as Matrix
 instances
                                  • 
 
                                • API: diagonals of feature synergy, redundancy, and association matrices are now
 nan instead of 1.0
                                  • 
-
                                • API: the leaf order of LinkageTree objects generated by
+
                                • API: the leaf order of LinkageTree objects generated by
 feature_…_linkage methods of LearnerInspector is now the same as the
-row and column order of Matrix objects returned by the
+row and column order of Matrix objects returned by the
 corresponding feature_…_matrix methods of LearnerInspector, minimizing
 the distance between adjacent leaves.
 The old sorting behaviour of FACET 1.x can be restored using method
-sort_by_weight()
                                  • 
+sort_by_weight()
                                  
 
                                
 
                                
 
                                
@@ -466,11 +477,11 @@ 
                                Other
 
 
                                
 
                                
@@ -482,29 +493,29 @@ 
                                FACET 1.2
-
                                1.2.3#
                                
+
                                
+
                                1.2.3#
                                
 
                                  
 
                                • BUILD: pin down matplotlib version to < 3.6 and scipy version to < 1.9 to ensure
 compatibility with pytools 1.2 and sklearndf 1.2
                                  • 
 
                                
 
                                
-
                                
-
                                1.2.2#
                                
+
                                
+
                                1.2.2#
                                
 
                                  
 
                                • catch up with FACET 1.1.2
                                  • 
 
                                
 
                                
-
                                
-
                                1.2.1#
                                
+
                                
+
                                1.2.1#
                                
 
 
                                
-
                                
-
                                1.2.0#
                                
+
                                
+
                                1.2.0#
                                
 
                                  
 
                                • BUILD: added support for sklearndf 1.2 and scikit-learn 0.24
                                  • 
 
                                • API: new optional parameter subsample in method
@@ -518,8 +529,8 @@ 
                                  1.2.0#<
 
                                  FACET 1.1#
                                  
 
                                  FACET 1.1 refines and enhances the association/synergy/redundancy calculations
 provided by the LearnerInspector.
                                  
-
                                  
-
                                  1.1.2#
                                  
+
                                  
+
                                  1.1.2#
                                  
 
 
                                  
-
                                  
-
                                  1.1.1#
                                  
+
                                  
+
                                  1.1.1#
                                  
 
                                    
 
                                  • DOC: add reference to FACET research paper on the project landing page
                                    • 
 
                                  • FIX: correctly count positive class frequency in UnivariateProbabilitySimulator
                                    • 
 
                                  
 
                                  
-
                                  
-
                                  1.1.0#
                                  
+
                                  
+
                                  1.1.0#
                                  
 
 
                                  
 
                                  
 
                                  
 
                                  FACET 1.0#
                                  
-
                                  
-
                                  1.0.3#
                                  
+
                                  
+
                                  1.0.3#
                                  
 
                                    
 
                                  • FIX: restrict package requirements to gamma-pytools 1.0 and
 sklearndf 1.0, since FACET 1.0 is not compatible with
 gamma-pytools 1.1
                                    • 
 
                                  
 
                                  
-
                                  
-
                                  1.0.2#
                                  
+
                                  
+
                                  1.0.2#
                                  
 
                                  This is a maintenance release focusing on enhancements to the CI/CD pipeline and bug
 fixes.
                                  
 
                                    
 
                                  • API: add support for shap 0.36 and 0.37 via a new
 BaseExplainer stub class
                                    • 
 
                                  • FIX: apply color scheme to the histogram section in SimulationMatplotStyle
                                    • 
-
                                  • BUILD: add support for numpy 1.20
                                    • 
+
                                  • BUILD: add support for numpy 1.20
                                    • 
 
                                  • BUILD: updates and changes to the CI/CD pipeline
                                    • 
 
                                  
 
                                  
-
                                  
-
                                  1.0.1#
                                  
+
                                  
+
                                  1.0.1#
                                  
 
                                  Initial release.
                                  
 
                                  
 
                                  
diff --git a/docs/docs-version/2-1/_modules/facet/simulation/viz/_style.html b/docs/docs-version/2-1/_modules/facet/simulation/viz/_style.html
index f95c03e0..f3a14b05 100644
--- a/docs/docs-version/2-1/_modules/facet/simulation/viz/_style.html
+++ b/docs/docs-version/2-1/_modules/facet/simulation/viz/_style.html
@@ -173,9 +173,10 @@ 
                                  Source code for facet.simulation.viz._style
                                   
 import logging
 from abc import ABCMeta, abstractmethod
-from typing import Any, Sequence, TextIO, Tuple, TypeVar, Union
+from typing import Any, Optional, Sequence, TextIO, Tuple, TypeVar, Union, cast
 
 from matplotlib.axes import Axes
+from matplotlib.transforms import Bbox
 from mpl_toolkits.axes_grid1 import make_axes_locatable
 from mpl_toolkits.axes_grid1.axes_divider import AxesDivider
 from mpl_toolkits.axes_grid1.axes_size import Scaled
@@ -372,27 +373,44 @@ 
                                  Source code for facet.simulation.viz._style
                                               y_min, y_max = main_ax.get_ylim()
             uplift_height = abs(y_max - y_min)
 
-            def _x_axis_height() -> float:
-                axis_below_size_pixels: float
-                _, axis_below_size_pixels = main_ax.get_xaxis().get_text_heights(
+            def _x_axis_height(padding: float) -> float:
+                # get the lower bound of the x axis in data space: this is the upper
+                # bound of the space below the x axis
+                x_axis_vertical_position: float = main_ax.get_ylim()[0]
+
+                # get the upper and lower vertical bound of the tick labels
+                xaxis_tight_bbox: Optional[Bbox] = main_ax.get_xaxis().get_tightbbox(
                     self.get_renderer()
                 )
-
-                y0: float
-                y1: float
-                ((_, y0), (_, y1)) = main_ax.transData.inverted().transform(
-                    ((0.0, 0.0), (0.0, axis_below_size_pixels))
+                if xaxis_tight_bbox is None:
+                    return 0.0
+
+                x_axis_labels_lower: float
+                x_axis_labels_upper: float
+                (
+                    x_axis_labels_lower,
+                    x_axis_labels_upper,
+                ) = main_ax.transData.inverted().transform(
+                    xaxis_tight_bbox.get_points()
+                )[
+                    :, 1
+                ]
+
+                # calculate the height, and pad it with the multiple of the tick
+                # label height provided in arg padding
+                return (
+                    max(x_axis_vertical_position - x_axis_labels_lower, 0)
+                    + (x_axis_labels_upper - x_axis_labels_lower) * padding
                 )
-                return abs(y1 - y0)
 
             # calculate the height of the x axis in data space; add additional padding
-            axis_below_size_data = _x_axis_height() * 1.2
+            axis_below_size_data = _x_axis_height(padding=0.5)
 
             # create the axes divider, then use it to append the new sub-axes at the
             # bottom while leaving sufficient padding in-between to accommodate the
             # main axes' x axis labels
             divider: AxesDivider = make_axes_locatable(main_ax)
-            return divider.append_axes(
+            sub_axes: Axes = divider.append_axes(
                 position="bottom",
                 size=Scaled(
                     uplift_height * SimulationMatplotStyle.__HISTOGRAM_SIZE_RATIO
@@ -403,6 +421,7 @@ 
                                  Source code for facet.simulation.viz._style
                                                       * (1 + SimulationMatplotStyle.__HISTOGRAM_SIZE_RATIO)
                 ),
             )
+            return sub_axes
 
         ax = _make_sub_axes()
 
@@ -415,10 +434,10 @@ 
                                  Source code for facet.simulation.viz._style
                                           # reduce the horizontal margin such that half a bar is to the left of the
         # leftmost tick mark (but the tick mark stays aligned with the main
         # simulation chart)
-        x_margin, _ = ax.margins()
+        x_margin, _ = cast(Tuple[float, float], ax.margins())
         ax.set_xmargin(
             max(
-                0,
+                0.0,
                 (width_bars / 2 - x_margin * (n_partitions - 1))
                 / (width_bars - (n_partitions - 1)),
             )
diff --git a/docs/docs-version/2-1/_sources/_generated/getting_started.rst.txt b/docs/docs-version/2-1/_sources/_generated/getting_started.rst.txt
index 443d291a..fd61bebe 100644
--- a/docs/docs-version/2-1/_sources/_generated/getting_started.rst.txt
+++ b/docs/docs-version/2-1/_sources/_generated/getting_started.rst.txt
@@ -4,19 +4,6 @@ Getting started with
 ====================
 .. image:: /_images/Gamma_Facet_Logo_RGB_LB.svg
 
-.. note:: **FACET 2.0 now available!**
-
-    FACET 2.0 brings numerous API enhancements and improvements, accelerates model
-    inspection by up to a factor of 50 in many practical applications, introduces a new,
-    more flexible and user-friendly API for hyperparameter tuning – with support for
-    `scikit-learn`'s native hyperparameter searchers – and improves the styling of all
-    visualizations.
-
-    See the `release notes `__
-    for more details.
-
-
-
 FACET is an open source library for human-explainable AI.
 It combines sophisticated model inspection and model-based simulation to enable better 
 explanations of your supervised machine learning models.
@@ -53,8 +40,8 @@ FACET is composed of the following key components:
 |                 | tried and tested pipelining paradigm with new capabilities for model  |
 |                 | selection, inspection, and simulation.                                |
 |                 | FACET also introduces                                                 |
-|                 | `sklearndf `__                |
-|                 | [`documentation `__]|
+|                 | `sklearndf `__                |
+|                 | [`documentation `__]|
 |                 | an augmented version of *scikit-learn* with enhanced support for      |
 |                 | *pandas* data frames that ensures end-to-end traceability of features.|
 +-----------------+-----------------------------------------------------------------------+
@@ -106,10 +93,10 @@ Quickstart
 The following quickstart guide provides a minimal example workflow to get you
 up and running with FACET.
 For additional tutorials and the API reference,
-see the `FACET documentation `__.
+see the `FACET documentation `__.
 
 Changes and additions to new versions are summarized in the
-`release notes `__.
+`release notes `__.
 
 
 Enhanced Machine Learning Workflow
@@ -348,7 +335,7 @@ removing `TCH`, and one of `TC` or `LDL` to further simplify the model and obtai
 reduced set of independent features.
 
 Please see the
-`API reference `__
+`API reference `__
 for more detail.
 
 
@@ -411,21 +398,21 @@ FACET is stable and is being supported long-term.
 
 Contributions to FACET are welcome and appreciated.
 For any bug reports or feature requests/enhancements please use the appropriate
-`GitHub form `_, and if you wish to do so,
+`GitHub form `_, and if you wish to do so,
 please open a PR addressing the issue.
 
 We do ask that for any major changes please discuss these with us first via an issue or
 using our team email: FacetTeam@bcg.com.
 
 For further information on contributing please see our
-`contribution guide `__.
+`contribution guide `__.
 
 
 License
 -------
 
 FACET is licensed under Apache 2.0 as described in the
-`LICENSE `_ file.
+`LICENSE `_ file.
 
 
 Acknowledgements
@@ -445,7 +432,7 @@ BCG GAMMA
 ---------
 
 If you would like to know more about the team behind FACET please see the
-`about us `__ page.
+`about us `__ page.
 
 We are always on the lookout for passionate and talented data scientists to join the
 BCG GAMMA team. If you would like to know more you can find out about
diff --git a/docs/docs-version/2-1/_sources/_generated/release_notes.rst.txt b/docs/docs-version/2-1/_sources/_generated/release_notes.rst.txt
index 45445a8d..b84e9f40 100644
--- a/docs/docs-version/2-1/_sources/_generated/release_notes.rst.txt
+++ b/docs/docs-version/2-1/_sources/_generated/release_notes.rst.txt
@@ -19,6 +19,12 @@ We still recommend using :mod:`sklearndf` models and learner pipelines along wit
 :class:`.NativeLearnerInspector` can be useful for inspecting models that have been
 trained using *scikit-learn* directly.
 
+2.1.1
+~~~~~
+
+This is a maintenance release to catch up with FACET |nbsp| 2.0.1.
+
+
 2.1.0
 ~~~~~
 
@@ -46,6 +52,8 @@ fully type-checked by |mypy|.
   and classifiers; it is no longer necessary to wrap them into a
   :class:`.RegressorPipelineDF` or :class:`.ClassifierPipelineDF` instance with empty
   preprocessing
+- FIX: replace a call to method ``get_text_heights()`` of :class:`matplotlib.axes.Axes`,
+  which is deprecated as of :mod:`matplotlib` |nbsp| 3.6
 
 
 2.0.0
diff --git a/docs/docs-version/2-1/_sources/contribution_guide.rst.txt b/docs/docs-version/2-1/_sources/contribution_guide.rst.txt
index 1f6467dd..c16306dd 100644
--- a/docs/docs-version/2-1/_sources/contribution_guide.rst.txt
+++ b/docs/docs-version/2-1/_sources/contribution_guide.rst.txt
@@ -296,7 +296,7 @@ The ``sphinx/source`` folder contains:
 
 
 The two key scripts are ``make.py`` and ``conf.py``. The base configuration for the
-these scripts can be found in `pytools/sphinx `_.
+these scripts can be found in `pytools/sphinx `_.
 The reason for this is to minimise code given the standardization of the documentation
 build across multiple packages.
 
@@ -452,7 +452,7 @@ The build process for the PyPI and conda distributions uses the following key
 files:
 
 - ``make.py``: generic Python script for package builds. Most configuration is imported
-  from pytools `make.py `__
+  from pytools `make.py `__
   which is a build script that wraps the package build, as well as exposing the matrix
   dependency definitions specified in the ``pyproject.toml`` as environment variables
 - ``pyproject.toml``: metadata for PyPI, build settings and package dependencies
diff --git a/docs/docs-version/2-1/_sources/faqs.rst.txt b/docs/docs-version/2-1/_sources/faqs.rst.txt
index fb62b425..ae554f83 100644
--- a/docs/docs-version/2-1/_sources/faqs.rst.txt
+++ b/docs/docs-version/2-1/_sources/faqs.rst.txt
@@ -14,7 +14,7 @@ on `stackoverflow `_.
 1. **What if I find a bug or have an idea for a new feature?**
 
     For bug reports or feature requests please use our
-    `GitHub issue tracker `_.
+    `GitHub issue tracker `_.
     For any other enquiries please feel free to contact us at FacetTeam@bcg.com.
 
 2. **How does FACET's novel algorithm calculate pairwise feature redundancy and synergy?**
diff --git a/docs/docs-version/2-1/_sources/tutorial/Classification_with_Facet.ipynb.txt b/docs/docs-version/2-1/_sources/tutorial/Classification_with_Facet.ipynb.txt
index 633dd449..9a61e41e 100644
--- a/docs/docs-version/2-1/_sources/tutorial/Classification_with_Facet.ipynb.txt
+++ b/docs/docs-version/2-1/_sources/tutorial/Classification_with_Facet.ipynb.txt
@@ -31,7 +31,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
     "\n",
     "***\n",
     "\n",
@@ -108,7 +108,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, etc.)\n",
     "2. Required FACET classes (inspection, selection, validation, simulation, etc.)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-Gamma/pytools)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-X-Official/pytools)) when using FACET"
    ]
   },
   {
@@ -162,7 +162,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
+    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
@@ -194,7 +194,7 @@
    "source": [
     "**pytools imports**\n",
     "\n",
-    "pytools (see on [GitHub](https://github.com/BCG-Gamma/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
+    "pytools (see on [GitHub](https://github.com/BCG-X-Official/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
    ]
   },
   {
@@ -1557,7 +1557,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For convenience when working in a non-notebook environment, all of the `Drawer`'s provided by the [pytools](https://github.com/BCG-Gamma/pytools) package also support a `style='text'` flag."
+    "For convenience when working in a non-notebook environment, all of the `Drawer`'s provided by the [pytools](https://github.com/BCG-X-Official/pytools) package also support a `style='text'` flag."
    ]
   },
   {
diff --git a/docs/docs-version/2-1/_sources/tutorial/Model_simulation_deep_dive.ipynb.txt b/docs/docs-version/2-1/_sources/tutorial/Model_simulation_deep_dive.ipynb.txt
index 6b10d66f..6a0d540d 100644
--- a/docs/docs-version/2-1/_sources/tutorial/Model_simulation_deep_dive.ipynb.txt
+++ b/docs/docs-version/2-1/_sources/tutorial/Model_simulation_deep_dive.ipynb.txt
@@ -40,7 +40,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.\n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.\n",
     "\n",
     "***\n",
     "\n",
@@ -117,7 +117,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, etc.)\n",
     "2. Required FACET classes (inspection, selection, validation, simulation, etc.)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-Gamma/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-Gamma/pytools)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-X-Official/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-X-Official/pytools)) when using FACET"
    ]
   },
   {
@@ -168,7 +168,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" *scikit-learn* package, we are going to use *sklearndf* (see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)). *sklearndf* is an open source library designed to address a common issue with *scikit-learn*: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. *sklearndf* retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard *scikit-learn* import:\n",
+    "Instead of using the \"regular\" *scikit-learn* package, we are going to use *sklearndf* (see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)). *sklearndf* is an open source library designed to address a common issue with *scikit-learn*: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. *sklearndf* retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard *scikit-learn* import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
@@ -193,7 +193,7 @@
    "source": [
     "**pytools imports**\n",
     "\n",
-    "pytools (see on [GitHub](https://github.com/BCG-Gamma/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
+    "pytools (see on [GitHub](https://github.com/BCG-X-Official/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
    ]
   },
   {
diff --git a/docs/docs-version/2-1/_sources/tutorial/Scikit-learn_classifier_summaries_using_FACET.ipynb.txt b/docs/docs-version/2-1/_sources/tutorial/Scikit-learn_classifier_summaries_using_FACET.ipynb.txt
index 00f821d3..02385b16 100644
--- a/docs/docs-version/2-1/_sources/tutorial/Scikit-learn_classifier_summaries_using_FACET.ipynb.txt
+++ b/docs/docs-version/2-1/_sources/tutorial/Scikit-learn_classifier_summaries_using_FACET.ipynb.txt
@@ -27,7 +27,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
     "\n",
     "***\n",
     "\n",
@@ -89,7 +89,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, sklearn, etc.)\n",
     "2. Required FACET classes (i.e., selection)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)) when using FACET"
    ]
   },
   {
@@ -151,7 +151,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
+    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
diff --git a/docs/docs-version/2-1/_sources/tutorial/Water_Drilling_Incident_Classification_with_Facet.ipynb.txt b/docs/docs-version/2-1/_sources/tutorial/Water_Drilling_Incident_Classification_with_Facet.ipynb.txt
index 6289d3e0..5a02f025 100644
--- a/docs/docs-version/2-1/_sources/tutorial/Water_Drilling_Incident_Classification_with_Facet.ipynb.txt
+++ b/docs/docs-version/2-1/_sources/tutorial/Water_Drilling_Incident_Classification_with_Facet.ipynb.txt
@@ -29,7 +29,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.       \n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.       \n",
     "\n",
     "***\n",
     "\n",
@@ -111,7 +111,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, etc.)\n",
     "2. Required FACET classes (inspection, selection, validation, simulation, etc.)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-Gamma/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-Gamma/pytools)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-X-Official/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-X-Official/pytools)) when using FACET"
    ]
   },
   {
@@ -191,7 +191,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
+    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
@@ -231,7 +231,7 @@
    "source": [
     "**pytools imports**\n",
     "\n",
-    "pytools (see on [GitHub](https://github.com/BCG-Gamma/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
+    "pytools (see on [GitHub](https://github.com/BCG-X-Official/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
    ]
   },
   {
diff --git a/docs/docs-version/2-1/_static/js/versions.js b/docs/docs-version/2-1/_static/js/versions.js
index 2178ec61..c87743e6 100644
--- a/docs/docs-version/2-1/_static/js/versions.js
+++ b/docs/docs-version/2-1/_static/js/versions.js
@@ -1,7 +1,7 @@
 const DOCS_VERSIONS = {
-    "current": "2.1.0",
+    "current": "2.1.1",
     "all": [
-        "2.1.0",
+        "2.1.1",
         "2.0.1",
         "1.2.3",
         "1.1.2",
diff --git a/docs/docs-version/2-1/_static/pygments.css b/docs/docs-version/2-1/_static/pygments.css
index f227e5c6..6110e9f1 100644
--- a/docs/docs-version/2-1/_static/pygments.css
+++ b/docs/docs-version/2-1/_static/pygments.css
@@ -22,6 +22,7 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
 .highlight .cs { color: #8f5902; font-style: italic } /* Comment.Special */
 .highlight .gd { color: #a40000 } /* Generic.Deleted */
 .highlight .ge { color: #000000; font-style: italic } /* Generic.Emph */
+.highlight .ges { color: #000000; font-weight: bold; font-style: italic } /* Generic.EmphStrong */
 .highlight .gr { color: #ef2929 } /* Generic.Error */
 .highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
 .highlight .gi { color: #00A000 } /* Generic.Inserted */
diff --git a/docs/docs-version/2-1/apidoc/facet/data/facet.data.Sample.html b/docs/docs-version/2-1/apidoc/facet/data/facet.data.Sample.html
index efe0a3e4..83f6c832 100644
--- a/docs/docs-version/2-1/apidoc/facet/data/facet.data.Sample.html
+++ b/docs/docs-version/2-1/apidoc/facet/data/facet.data.Sample.html
@@ -601,12 +601,12 @@ 
                                  facet.data.SampleDataFrame.
                                  
+
                                  The underlying data structure is a DataFrame.
                                  
 
                                  Supports len(), returning the number of observations in this sample.
                                  
 
                                  
 
                                  Parameters
                                  
 
                                  
 
 
                                  
 
                                  
-property index: pandas.Index#
                                  
+property index: pandas.Index#
 
                                  Row index of all observations in this sample.
                                  
 
                                  
 
                                  Return type
                                  
-
                                  Index
                                  
+
                                  Index
                                  
 
                                  
 
                                  
 
                                  
 
 
                                  
 
                                  
-property target: Union[pandas.Series, pandas.DataFrame]#
                                  
+property target: Union[pandas.Series, pandas.DataFrame]#
 
                                  The target variable(s) for all observations.
                                  
 
                                  Represented as a series if there is only a single target, or as a data frame if
 there are multiple targets.
                                  
 
                                  
 
                                  Return type
                                  
-
                                  Union[Series, DataFrame]
                                  
+
                                  Union[Series, DataFrame]
                                  
 
                                  
 
                                  
 
                                  
@@ -816,12 +816,12 @@ 
                                  facet.data.Sample
 
                                  
-property weight: Optional[pandas.Series]#
                                  
+property weight: Optional[pandas.Series]#
 
                                  A series indicating the weight for each observation; None if no weights
 are defined.
                                  
 
                                  
 
                                  Return type
                                  
-
                                  Optional[Series]
                                  
+
                                  Optional[Series]
                                  
 
                                  
 
                                  
 
                                  
diff --git a/docs/docs-version/2-1/apidoc/facet/data/partition/facet.data.partition.CategoryPartitioner.html b/docs/docs-version/2-1/apidoc/facet/data/partition/facet.data.partition.CategoryPartitioner.html
index a61f81fc..5c911dce 100644
--- a/docs/docs-version/2-1/apidoc/facet/data/partition/facet.data.partition.CategoryPartitioner.html
+++ b/docs/docs-version/2-1/apidoc/facet/data/partition/facet.data.partition.CategoryPartitioner.html
@@ -657,7 +657,7 @@ 
                                  facet.data.partition.CategoryPartitioner
 
                                  Parameters
                                  
 
                                    
-
                                  • values (ndarray[Any, dtype[Any]]) – a sequence of observed values as the empirical basis for
+
                                  • values (ndarray[Any, dtype[Any]]) – a sequence of observed values as the empirical basis for
 calculating the partitions
                                    • 
 
                                  • fit_params (Any) – optional fitting parameters
                                    • 
 
                                  
@@ -673,11 +673,11 @@ 
                                  facet.data.partition.CategoryPartitioner
 
                                  
-property frequencies_: numpy.ndarray[Any, numpy.dtype[numpy.int64]]#
                                  
+property frequencies_: numpy.ndarray[Any, numpy.dtype[numpy.int64]]#
 
                                  The count of values allocated to each partition.
                                  
 
                                  
 
                                  Return type
                                  
-
                                  ndarray[Any, dtype[int64]]
                                  
+
                                  ndarray[Any, dtype[int64]]
                                  
 
                                  
 
                                  
 
                                  
@@ -721,7 +721,7 @@ 
                                  facet.data.partition.CategoryPartitioner
 
                                  Return type
                                  
-
                                  Sequence[TypeVar(T_Values, bound= generic)]
                                  
+
                                  Sequence[TypeVar(T_Values, bound= generic)]
                                  
 
                                  
 
 
                                  
diff --git a/docs/docs-version/2-1/apidoc/facet/data/partition/facet.data.partition.ContinuousRangePartitioner.html b/docs/docs-version/2-1/apidoc/facet/data/partition/facet.data.partition.ContinuousRangePartitioner.html
index bbb2e4f4..dc9f58ed 100644
--- a/docs/docs-version/2-1/apidoc/facet/data/partition/facet.data.partition.ContinuousRangePartitioner.html
+++ b/docs/docs-version/2-1/apidoc/facet/data/partition/facet.data.partition.ContinuousRangePartitioner.html
@@ -682,7 +682,7 @@ 
                                  facet.data.partition.ContinuousRangePartitioner
 
                                  Parameters
                                  
 
                                  
diff --git a/docs/docs-version/2-1/apidoc/facet/data/partition/facet.data.partition.IntegerRangePartitioner.html b/docs/docs-version/2-1/apidoc/facet/data/partition/facet.data.partition.IntegerRangePartitioner.html
index b7788e90..f0e1af6f 100644
--- a/docs/docs-version/2-1/apidoc/facet/data/partition/facet.data.partition.IntegerRangePartitioner.html
+++ b/docs/docs-version/2-1/apidoc/facet/data/partition/facet.data.partition.IntegerRangePartitioner.html
@@ -682,7 +682,7 @@ 
                                  facet.data.partition.IntegerRangePartitioner
 
                                  Parameters
                                  
 
                                  
diff --git a/docs/docs-version/2-1/apidoc/facet/data/partition/facet.data.partition.Partitioner.html b/docs/docs-version/2-1/apidoc/facet/data/partition/facet.data.partition.Partitioner.html
index 286a71e2..01bd47a7 100644
--- a/docs/docs-version/2-1/apidoc/facet/data/partition/facet.data.partition.Partitioner.html
+++ b/docs/docs-version/2-1/apidoc/facet/data/partition/facet.data.partition.Partitioner.html
@@ -598,10 +598,10 @@ 
                                  facet.data.partition.Partitioner
 
                                  Bases
                                  
-
                                  FittableMixin [ndarray [Any, dtype [~T_Values]]]
                                  
+
                                  FittableMixin [ndarray [Any, dtype [~T_Values]]]
                                  
 
                                  
 
                                  Generic types
                                  
-
                                  ~T_Values(bound= generic)
                                  
+
                                  ~T_Values(bound= generic)
                                  
 
                                  
 
                                  Metaclasses
                                  
 
                                  ABCMeta
                                  
@@ -640,7 +640,7 @@ 
                                  facet.data.partition.PartitionerTrue if this is partitioner handles categorical values, False otherwise.
                                  
 
 
                                  is_fitted
                                  
-
                                  See pytools.fit.FittableMixin.is_fitted
                                  
+
                                  See pytools.fit.FittableMixin.is_fitted
                                  
 
 
                                  max_partitions
                                  
 
                                  The maximum number of partitions to be generated by this partitioner.
                                  
@@ -658,7 +658,7 @@ 
                                  facet.data.partition.Partitioner
 
                                  Parameters
                                  
 
                                  
@@ -674,11 +674,11 @@ 
                                  facet.data.partition.Partitioner
 
                                  
-property frequencies_: numpy.ndarray[Any, numpy.dtype[numpy.int64]]#
                                  
+property frequencies_: numpy.ndarray[Any, numpy.dtype[numpy.int64]]#
 
                                  The count of values allocated to each partition.
                                  
 
                                  
 
                                  Return type
                                  
-
                                  ndarray[Any, dtype[int64]]
                                  
+
                                  ndarray[Any, dtype[int64]]
                                  
 
                                  
 
                                  
 
                                  
@@ -697,7 +697,7 @@ 
                                  facet.data.partition.Partitioner
 
                                  
 abstract property is_fitted: bool#
                                  
-
                                  See pytools.fit.FittableMixin.is_fitted
                                  
+
                                  See pytools.fit.FittableMixin.is_fitted
                                  
 
                                  
 
 
                                  
@@ -717,7 +717,7 @@ 
                                  facet.data.partition.Partitioner
 
                                  Return type
                                  
-
                                  Sequence[TypeVar(T_Values, bound= generic)]
                                  
+
                                  Sequence[TypeVar(T_Values, bound= generic)]
                                  
 
                                  
 
                                  
 
                                  
diff --git a/docs/docs-version/2-1/apidoc/facet/data/partition/facet.data.partition.RangePartitioner.html b/docs/docs-version/2-1/apidoc/facet/data/partition/facet.data.partition.RangePartitioner.html
index 60cbe794..44172036 100644
--- a/docs/docs-version/2-1/apidoc/facet/data/partition/facet.data.partition.RangePartitioner.html
+++ b/docs/docs-version/2-1/apidoc/facet/data/partition/facet.data.partition.RangePartitioner.html
@@ -672,7 +672,7 @@ 
                                  facet.data.partition.RangePartitioner
 
                                  Parameters
                                  
 
                                  
diff --git a/docs/docs-version/2-1/apidoc/facet/explanation/base/facet.explanation.base.BaseExplainer.html b/docs/docs-version/2-1/apidoc/facet/explanation/base/facet.explanation.base.BaseExplainer.html
index f1c7641a..a65421fd 100644
--- a/docs/docs-version/2-1/apidoc/facet/explanation/base/facet.explanation.base.BaseExplainer.html
+++ b/docs/docs-version/2-1/apidoc/facet/explanation/base/facet.explanation.base.BaseExplainer.html
@@ -613,7 +613,7 @@ 
                                  facet.explanation.base.BaseExplainershap 0.36, the old API is deprecated for the majority of explainers
-while the shap.KernelExplainer still uses the old API exclusively
+while the shap.KernelExplainer still uses the old API exclusively
 in shap 0.41.
 We remedy this by adding support for both APIs to all explainers created through
 an ExplainerFactory object.
                                  
@@ -702,15 +702,15 @@ 
                                  facet.explanation.base.BaseExplainer
 
                                  Parameters
                                  
 
                                  
 
                                  
 
                                  Return type
                                  
-
                                  Union[ndarray[Any, dtype[float64]], List[ndarray[Any, dtype[float64]]]]
                                  
+
                                  Union[ndarray[Any, dtype[float64]], List[ndarray[Any, dtype[float64]]]]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  SHAP values as an array of shape
@@ -727,15 +727,15 @@ 
                                  facet.explanation.base.BaseExplainer
 
                                  Parameters
                                  
 
                                  
 
                                  
 
                                  Return type
                                  
-
                                  Union[ndarray[Any, dtype[float64]], List[ndarray[Any, dtype[float64]]]]
                                  
+
                                  Union[ndarray[Any, dtype[float64]], List[ndarray[Any, dtype[float64]]]]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  SHAP values as an array of shape (n_observations, n_features);
diff --git a/docs/docs-version/2-1/apidoc/facet/explanation/base/facet.explanation.base.ExplainerFactory.html b/docs/docs-version/2-1/apidoc/facet/explanation/base/facet.explanation.base.ExplainerFactory.html
index 5dcc61f2..87c3c612 100644
--- a/docs/docs-version/2-1/apidoc/facet/explanation/base/facet.explanation.base.ExplainerFactory.html
+++ b/docs/docs-version/2-1/apidoc/facet/explanation/base/facet.explanation.base.ExplainerFactory.html
@@ -598,7 +598,7 @@ 
                                  facet.explanation.base.ExplainerFactoryExplainer objects.
                                  
 
                                  
 
                                  Bases
                                  
-
                                  HasExpressionRepr
                                  
+
                                  HasExpressionRepr
                                  
 
                                  
 
                                  Generic types
                                  
 
                                  ~T_Model
                                  
@@ -622,7 +622,7 @@ 
                                  facet.explanation.base.ExplainerFactoryExplainer to compute shap values.
                                  
 
 
                                  to_expression
                                  
-
                                  See pytools.expression.HasExpressionRepr.to_expression()
                                  
+
                                  See pytools.expression.HasExpressionRepr.to_expression()
                                  
 
 
 
@@ -656,7 +656,7 @@ 
                                  facet.explanation.base.ExplainerFactoryParameters
 
                                  
 
                                  
 
                                  Return type
                                  
@@ -671,7 +671,7 @@ 
                                  facet.explanation.base.ExplainerFactory
 
                                  
 abstract to_expression()#
                                  
-
                                  See pytools.expression.HasExpressionRepr.to_expression()
                                  
+
                                  See pytools.expression.HasExpressionRepr.to_expression()
                                  
 
                                  
 
 
                                  
diff --git a/docs/docs-version/2-1/apidoc/facet/explanation/facet.explanation.ExactExplainerFactory.html b/docs/docs-version/2-1/apidoc/facet/explanation/facet.explanation.ExactExplainerFactory.html
index c096f381..0d0dc960 100644
--- a/docs/docs-version/2-1/apidoc/facet/explanation/facet.explanation.ExactExplainerFactory.html
+++ b/docs/docs-version/2-1/apidoc/facet/explanation/facet.explanation.ExactExplainerFactory.html
@@ -655,8 +655,8 @@ 
                                  facet.explanation.ExactExplainerFactory
 
                                  Parameters
                                  
 
                                  
 
                                  
 
                                  Return type
                                  
@@ -675,8 +675,8 @@ 
                                  facet.explanation.ExactExplainerFactory
 
                                  Parameters
                                  
 
                                  
 
                                  
 
                                  Return type
                                  
@@ -694,7 +694,7 @@ 
                                  facet.explanation.ExactExplainerFactory
 
                                  Return type
                                  
-
                                  Expression
                                  
+
                                  Expression
                                  
 
                                  
 
                                  Returns
                                  
 
                                  the expression representing this object
                                  
diff --git a/docs/docs-version/2-1/apidoc/facet/explanation/facet.explanation.FunctionExplainerFactory.html b/docs/docs-version/2-1/apidoc/facet/explanation/facet.explanation.FunctionExplainerFactory.html
index ec3b9ef2..2463300a 100644
--- a/docs/docs-version/2-1/apidoc/facet/explanation/facet.explanation.FunctionExplainerFactory.html
+++ b/docs/docs-version/2-1/apidoc/facet/explanation/facet.explanation.FunctionExplainerFactory.html
@@ -599,7 +599,7 @@ 
                                  facet.explanation.FunctionExplainerFactory
 
                                  Bases
                                  
-
                                  ExplainerFactory [Union [RegressorMixin, ClassifierMixin, Callable [[typing.Union[pandas.core.series.Series, pandas.core.frame.DataFrame, numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]]], Union [Series, ndarray [Any, dtype [float64]], float]]]]
                                  
+
                                  ExplainerFactory [Union [RegressorMixin, ClassifierMixin, Callable [[typing.Union[pandas.core.series.Series, pandas.core.frame.DataFrame, numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]]], Union [Series, ndarray [Any, dtype [float64]], float]]]]
                                  
 
                                  
 
                                  Metaclasses
                                  
 
                                  ABCMeta
                                  
@@ -623,7 +623,7 @@ 
                                  facet.explanation.FunctionExplainerFactory
                                  to_expression
                                  
-
                                  See pytools.expression.HasExpressionRepr.to_expression()
                                  
+
                                  See pytools.expression.HasExpressionRepr.to_expression()
                                  
 
 
 
@@ -656,8 +656,8 @@ 
                                  facet.explanation.FunctionExplainerFactory
 
                                  Parameters
                                  
 
                                  
 
                                  
 
                                  Return type
                                  
@@ -676,8 +676,8 @@ 
                                  facet.explanation.FunctionExplainerFactory
 
                                  Parameters
                                  
 
                                  
 
                                  
 
                                  Return type
                                  
@@ -692,7 +692,7 @@ 
                                  facet.explanation.FunctionExplainerFactory
 
                                  
 abstract to_expression()#
                                  
-
                                  See pytools.expression.HasExpressionRepr.to_expression()
                                  
+
                                  See pytools.expression.HasExpressionRepr.to_expression()
                                  
 
                                  
 
 
                                  
diff --git a/docs/docs-version/2-1/apidoc/facet/explanation/facet.explanation.KernelExplainerFactory.html b/docs/docs-version/2-1/apidoc/facet/explanation/facet.explanation.KernelExplainerFactory.html
index 644ecf81..0c0a3b16 100644
--- a/docs/docs-version/2-1/apidoc/facet/explanation/facet.explanation.KernelExplainerFactory.html
+++ b/docs/docs-version/2-1/apidoc/facet/explanation/facet.explanation.KernelExplainerFactory.html
@@ -595,7 +595,7 @@ 
                                  facet.explanation.KernelExplainerFactory
 
                                  
 class facet.explanation.KernelExplainerFactory(*, link=None, l1_reg='num_features(10)', data_size_limit=100, **explainer_kwargs)[source]#
                                  
-
                                  A factory constructing KernelExplainer instances.
                                  
+
                                  A factory constructing KernelExplainer instances.
                                  
 
                                  
 
                                  Bases
                                  
 
                                  FunctionExplainerFactory
                                  
@@ -607,8 +607,8 @@ 
                                  facet.explanation.KernelExplainerFactory
                                  
 
diff --git a/docs/docs-version/2-1/apidoc/facet/explanation/parallel/facet.explanation.parallel.ExplainerQueue.html b/docs/docs-version/2-1/apidoc/facet/explanation/parallel/facet.explanation.parallel.ExplainerQueue.html
index dacc1369..41f4ab7d 100644
--- a/docs/docs-version/2-1/apidoc/facet/explanation/parallel/facet.explanation.parallel.ExplainerQueue.html
+++ b/docs/docs-version/2-1/apidoc/facet/explanation/parallel/facet.explanation.parallel.ExplainerQueue.html
@@ -598,7 +598,7 @@ 
                                  facet.explanation.parallel.ExplainerQueue
 
                                  Bases
                                  
-
                                  JobQueue [Union [ndarray [Any, dtype [Any]], List [ndarray [Any, dtype [Any]]]], Union [ndarray [Any, dtype [Any]], List [ndarray [Any, dtype [Any]]]]]
                                  
+
                                  JobQueue [Union [ndarray [Any, dtype [Any]], List [ndarray [Any, dtype [Any]]]], Union [ndarray [Any, dtype [Any]], List [ndarray [Any, dtype [Any]]]]]
                                  
 
                                  
 
                                  Metaclasses
                                  
 
                                  ABCMeta
                                  
@@ -606,8 +606,8 @@ 
                                  facet.explanation.parallel.ExplainerQueueParameters
 
                                  
 
 
                                  
 
                                  
-X: numpy.ndarray[Any, numpy.dtype[Any]]#
                                  
+X: numpy.ndarray[Any, numpy.dtype[Any]]#
 
                                  the feature values of the observations to be explained
                                  
 
                                  
 
@@ -739,7 +739,7 @@ 
                                  facet.explanation.parallel.ExplainerQueue
 
                                  
-y: Optional[numpy.ndarray[Any, numpy.dtype[Any]]]#
                                  
+y: Optional[numpy.ndarray[Any, numpy.dtype[Any]]]#
 
                                  the target values of the observations to be explained
                                  
 
                                  
 
diff --git a/docs/docs-version/2-1/apidoc/facet/explanation/parallel/facet.explanation.parallel.ParallelExplainer.html b/docs/docs-version/2-1/apidoc/facet/explanation/parallel/facet.explanation.parallel.ParallelExplainer.html
index 5027b086..e17a1b67 100644
--- a/docs/docs-version/2-1/apidoc/facet/explanation/parallel/facet.explanation.parallel.ParallelExplainer.html
+++ b/docs/docs-version/2-1/apidoc/facet/explanation/parallel/facet.explanation.parallel.ParallelExplainer.html
@@ -601,7 +601,7 @@ 
                                  facet.explanation.parallel.ParallelExplainer
 
                                  Bases
                                  
-
                                  ParallelizableMixin, BaseExplainer
                                  
+
                                  ParallelizableMixin, BaseExplainer
                                  
 
                                  
 
                                  Metaclasses
                                  
 
                                  ABCMeta
                                  
@@ -725,15 +725,15 @@ 
                                  facet.explanation.parallel.ParallelExplainer
 
                                  Parameters
                                  
 
                                  
 
                                  
 
                                  Return type
                                  
-
                                  Union[ndarray[Any, dtype[float64]], List[ndarray[Any, dtype[float64]]]]
                                  
+
                                  Union[ndarray[Any, dtype[float64]], List[ndarray[Any, dtype[float64]]]]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  SHAP values as an array of shape
@@ -750,15 +750,15 @@ 
                                  facet.explanation.parallel.ParallelExplainer
 
                                  Parameters
                                  
 
                                  
 
                                  
 
                                  Return type
                                  
-
                                  Union[ndarray[Any, dtype[float64]], List[ndarray[Any, dtype[float64]]]]
                                  
+
                                  Union[ndarray[Any, dtype[float64]], List[ndarray[Any, dtype[float64]]]]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  SHAP values as an array of shape (n_observations, n_features);
diff --git a/docs/docs-version/2-1/apidoc/facet/facet.explanation.html b/docs/docs-version/2-1/apidoc/facet/facet.explanation.html
index 0a47e6a0..79f6ac72 100644
--- a/docs/docs-version/2-1/apidoc/facet/facet.explanation.html
+++ b/docs/docs-version/2-1/apidoc/facet/facet.explanation.html
@@ -626,13 +626,13 @@ 
                                  ClassesExplainer instances that use Python functions as the underlying model.
                                  
 
 
                                  KernelExplainerFactory
                                  
-
                                  A factory constructing KernelExplainer instances.
                                  
+
                                  A factory constructing KernelExplainer instances.
                                  
 
 
                                  PermutationExplainerFactory
                                  
 
                                  A factory constructing Permutation explainer instances.
                                  
 
 
                                  TreeExplainerFactory
                                  
-
                                  A factory constructing TreeExplainer instances.
                                  
+
                                  A factory constructing TreeExplainer instances.
                                  
 
 
 
diff --git a/docs/docs-version/2-1/apidoc/facet/facet.inspection.html b/docs/docs-version/2-1/apidoc/facet/facet.inspection.html
index 9165aa8f..d50993a0 100644
--- a/docs/docs-version/2-1/apidoc/facet/facet.inspection.html
+++ b/docs/docs-version/2-1/apidoc/facet/facet.inspection.html
@@ -623,7 +623,7 @@ 
                                  Classes
                                  LearnerInspector
                                  
-
                                  Explain an sklearndf regressor or classifier based on SHAP values.
                                  
+
                                  Explain an sklearndf regressor or classifier based on SHAP values.
                                  
 
 
                                  NativeLearnerInspector
                                  
 
                                  Explain a native scikit-learn regressor or classifier based on SHAP values.
                                  
diff --git a/docs/docs-version/2-1/apidoc/facet/inspection/base/facet.inspection.base.ModelInspector.html b/docs/docs-version/2-1/apidoc/facet/inspection/base/facet.inspection.base.ModelInspector.html
index 2e65bda3..7faa9dca 100644
--- a/docs/docs-version/2-1/apidoc/facet/inspection/base/facet.inspection.base.ModelInspector.html
+++ b/docs/docs-version/2-1/apidoc/facet/inspection/base/facet.inspection.base.ModelInspector.html
@@ -630,7 +630,7 @@ 
                                  facet.inspection.base.ModelInspector
 
                                  Bases
                                  
-
                                  ParallelizableMixin, FittableMixin [Sample]
                                  
+
                                  FittableMixin [Sample], ParallelizableMixin
                                  
 
                                  
 
                                  Generic types
                                  
 
                                  ~T_Model
                                  
@@ -761,7 +761,7 @@ 
                                  facet.inspection.base.ModelInspector
 
                                  Return type
                                  
-
                                  Union[LinkageTree, List[LinkageTree]]
                                  
+
                                  Union[LinkageTree, List[LinkageTree]]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  linkage tree of feature associations; list of linkage trees
@@ -803,7 +803,7 @@ 
                                  facet.inspection.base.ModelInspectorReturn type
-
                                  Union[Matrix[float64], List[Matrix[float64]]]
                                  
+
                                  Union[Matrix[float64], List[Matrix[float64]]]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  feature association matrix as a data frame of shape
@@ -826,7 +826,7 @@ 
                                  facet.inspection.base.ModelInspectorReturn type
-
                                  Union[Series, DataFrame]
                                  
+
                                  Union[Series, DataFrame]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  a series of length n_features for single-output models, or a
@@ -872,7 +872,7 @@ 
                                  facet.inspection.base.ModelInspector
 
                                  Return type
                                  
-
                                  Union[Matrix[float64], List[Matrix[float64]]]
                                  
+
                                  Union[Matrix[float64], List[Matrix[float64]]]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  relative shap interaction values as a data frame of shape
@@ -891,7 +891,7 @@ 
                                  facet.inspection.base.ModelInspector
 
                                  Return type
                                  
-
                                  Union[LinkageTree, List[LinkageTree]]
                                  
+
                                  Union[LinkageTree, List[LinkageTree]]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  linkage tree of feature redundancies; list of linkage trees
@@ -932,7 +932,7 @@ 
                                  facet.inspection.base.ModelInspectorReturn type
-
                                  Union[Matrix[float64], List[Matrix[float64]]]
                                  
+
                                  Union[Matrix[float64], List[Matrix[float64]]]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  feature redundancy matrix as a data frame of shape
@@ -951,7 +951,7 @@ 
                                  facet.inspection.base.ModelInspector
 
                                  Return type
                                  
-
                                  Union[LinkageTree, List[LinkageTree]]
                                  
+
                                  Union[LinkageTree, List[LinkageTree]]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  linkage tree of feature synergies; list of linkage trees
@@ -992,7 +992,7 @@ 
                                  facet.inspection.base.ModelInspectorReturn type
-
                                  Union[Matrix[float64], List[Matrix[float64]]]
                                  
+
                                  Union[Matrix[float64], List[Matrix[float64]]]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  feature synergy matrix as a data frame of shape
@@ -1034,10 +1034,10 @@ 
                                  facet.inspection.base.ModelInspector
 
                                  Parameters
                                  
-
                                  features (Union[DataFrame, Series]) – features to preprocess
                                  
+
                                  features (Union[DataFrame, Series]) – features to preprocess
                                  
 
                                  
 
                                  Return type
                                  
-
                                  DataFrame
                                  
+
                                  DataFrame
                                  
 
                                  
 
                                  Returns
                                  
 
                                  preprocessed features
                                  
@@ -1055,7 +1055,7 @@ 
                                  facet.inspection.base.ModelInspector
 
                                  Return type
                                  
-
                                  Union[DataFrame, List[DataFrame]]
                                  
+
                                  Union[DataFrame, List[DataFrame]]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  a data frame with SHAP interaction values
                                  
@@ -1107,7 +1107,7 @@ 
                                  facet.inspection.base.ModelInspector
 
                                  Return type
                                  
-
                                  Union[DataFrame, List[DataFrame]]
                                  
+
                                  Union[DataFrame, List[DataFrame]]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  a data frame with SHAP values
                                  
diff --git a/docs/docs-version/2-1/apidoc/facet/inspection/facet.inspection.FunctionInspector.html b/docs/docs-version/2-1/apidoc/facet/inspection/facet.inspection.FunctionInspector.html
index 40f9cd31..386f3689 100644
--- a/docs/docs-version/2-1/apidoc/facet/inspection/facet.inspection.FunctionInspector.html
+++ b/docs/docs-version/2-1/apidoc/facet/inspection/facet.inspection.FunctionInspector.html
@@ -628,7 +628,7 @@ 
                                  facet.inspection.FunctionInspector
                                  ModelInspector [~T_Function]
                                  
 
                                  
 
                                  Generic types
                                  
-
                                  ~T_Function(bound= Callable [[typing.Union[pandas.core.series.Series, pandas.core.frame.DataFrame, numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]]], Union [Series, ndarray [Any, dtype [float64]], float]])
                                  
+
                                  ~T_Function(bound= Callable [[typing.Union[pandas.core.series.Series, pandas.core.frame.DataFrame, numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]]], Union [Series, ndarray [Any, dtype [float64]], float]])
                                  
 
                                  
 
                                  Metaclasses
                                  
 
                                  ABCMeta
                                  
@@ -767,7 +767,7 @@ 
                                  facet.inspection.FunctionInspector
 
                                  Return type
                                  
-
                                  Union[LinkageTree, List[LinkageTree]]
                                  
+
                                  Union[LinkageTree, List[LinkageTree]]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  linkage tree of feature associations; list of linkage trees
@@ -809,7 +809,7 @@ 
                                  facet.inspection.FunctionInspectorReturn type
-
                                  Union[Matrix[float64], List[Matrix[float64]]]
                                  
+
                                  Union[Matrix[float64], List[Matrix[float64]]]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  feature association matrix as a data frame of shape
@@ -832,7 +832,7 @@ 
                                  facet.inspection.FunctionInspectorReturn type
-
                                  Union[Series, DataFrame]
                                  
+
                                  Union[Series, DataFrame]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  a series of length n_features for single-output models, or a
@@ -878,7 +878,7 @@ 
                                  facet.inspection.FunctionInspector
 
                                  Return type
                                  
-
                                  Union[Matrix[float64], List[Matrix[float64]]]
                                  
+
                                  Union[Matrix[float64], List[Matrix[float64]]]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  relative shap interaction values as a data frame of shape
@@ -897,7 +897,7 @@ 
                                  facet.inspection.FunctionInspector
 
                                  Return type
                                  
-
                                  Union[LinkageTree, List[LinkageTree]]
                                  
+
                                  Union[LinkageTree, List[LinkageTree]]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  linkage tree of feature redundancies; list of linkage trees
@@ -938,7 +938,7 @@ 
                                  facet.inspection.FunctionInspectorReturn type
-
                                  Union[Matrix[float64], List[Matrix[float64]]]
                                  
+
                                  Union[Matrix[float64], List[Matrix[float64]]]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  feature redundancy matrix as a data frame of shape
@@ -957,7 +957,7 @@ 
                                  facet.inspection.FunctionInspector
 
                                  Return type
                                  
-
                                  Union[LinkageTree, List[LinkageTree]]
                                  
+
                                  Union[LinkageTree, List[LinkageTree]]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  linkage tree of feature synergies; list of linkage trees
@@ -998,7 +998,7 @@ 
                                  facet.inspection.FunctionInspectorReturn type
-
                                  Union[Matrix[float64], List[Matrix[float64]]]
                                  
+
                                  Union[Matrix[float64], List[Matrix[float64]]]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  feature synergy matrix as a data frame of shape
@@ -1040,10 +1040,10 @@ 
                                  facet.inspection.FunctionInspector
 
                                  Parameters
                                  
-
                                  features (Union[DataFrame, Series]) – features to preprocess
                                  
+
                                  features (Union[DataFrame, Series]) – features to preprocess
                                  
 
                                  
 
                                  Return type
                                  
-
                                  DataFrame
                                  
+
                                  DataFrame
                                  
 
                                  
 
                                  Returns
                                  
 
                                  preprocessed features
                                  
@@ -1061,7 +1061,7 @@ 
                                  facet.inspection.FunctionInspector
 
                                  Return type
                                  
-
                                  Union[DataFrame, List[DataFrame]]
                                  
+
                                  Union[DataFrame, List[DataFrame]]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  a data frame with SHAP interaction values
                                  
@@ -1113,7 +1113,7 @@ 
                                  facet.inspection.FunctionInspector
 
                                  Return type
                                  
-
                                  Union[DataFrame, List[DataFrame]]
                                  
+
                                  Union[DataFrame, List[DataFrame]]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  a data frame with SHAP values
                                  
diff --git a/docs/docs-version/2-1/apidoc/facet/inspection/facet.inspection.LearnerInspector.html b/docs/docs-version/2-1/apidoc/facet/inspection/facet.inspection.LearnerInspector.html
index b3f800f3..783c7c27 100644
--- a/docs/docs-version/2-1/apidoc/facet/inspection/facet.inspection.LearnerInspector.html
+++ b/docs/docs-version/2-1/apidoc/facet/inspection/facet.inspection.LearnerInspector.html
@@ -597,7 +597,7 @@ 
                                  facet.inspection.LearnerInspector
 
                                  
 class facet.inspection.LearnerInspector(model, *, explainer_factory=None, shap_interaction=True, n_jobs=None, shared_memory=None, pre_dispatch=None, verbose=None)[source]#
                                  
-
                                  Explain an sklearndf regressor or classifier based on SHAP values.
                                  
+
                                  Explain an sklearndf regressor or classifier based on SHAP values.
                                  
 
                                  Focus is on explaining the overall model, but the inspector also delivers
 SHAP explanations of the individual observations.
                                  
 
                                  Available inspection methods are:
                                  
@@ -628,7 +628,7 @@ 
                                  facet.inspection.LearnerInspector
                                  ModelInspector [~T_SupervisedLearner]
                                  
 
                                  
 
                                  Generic types
                                  
-
                                  ~T_SupervisedLearnerDF(bound= SupervisedLearnerDF)
                                  
+
                                  ~T_SupervisedLearnerDF(bound= SupervisedLearnerDF)
                                  
 
                                  
 
                                  Metaclasses
                                  
 
                                  ABCMeta
                                  
@@ -636,8 +636,8 @@ 
                                  facet.inspection.LearnerInspectorParameters
 
                                  
 
                                  
@@ -641,9 +641,9 @@ 
                                  facet.validation.StationaryBootstrapCV
 
                                  Parameters
                                  
 
                                  
 
                                  
 
                                  Return type
                                  
@@ -662,14 +662,14 @@ 
                                  facet.validation.StationaryBootstrapCV
 
                                  Parameters
                                  
 
                                  
 
                                  
 
                                  Return type
                                  
-
                                  Generator[Tuple[ndarray[Any, dtype[int64]], ndarray[Any, dtype[int64]]], None, None]
                                  
+
                                  Generator[Tuple[ndarray[Any, dtype[int64]], ndarray[Any, dtype[int64]]], None, None]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  a generator yielding (train, test) tuples where
diff --git a/docs/docs-version/2-1/apidoc/facet/validation/facet.validation.StratifiedBootstrapCV.html b/docs/docs-version/2-1/apidoc/facet/validation/facet.validation.StratifiedBootstrapCV.html
index e75b2aed..30794714 100644
--- a/docs/docs-version/2-1/apidoc/facet/validation/facet.validation.StratifiedBootstrapCV.html
+++ b/docs/docs-version/2-1/apidoc/facet/validation/facet.validation.StratifiedBootstrapCV.html
@@ -612,7 +612,7 @@ 
                                  facet.validation.StratifiedBootstrapCVParameters
 
                                  
 
                                  
@@ -640,9 +640,9 @@ 
                                  facet.validation.StratifiedBootstrapCV
 
                                  Parameters
                                  
 
                                  
 
                                  
 
                                  Return type
                                  
@@ -661,14 +661,14 @@ 
                                  facet.validation.StratifiedBootstrapCV
 
                                  Parameters
                                  
 
                                  
 
                                  
 
                                  Return type
                                  
-
                                  Generator[Tuple[ndarray[Any, dtype[int64]], ndarray[Any, dtype[int64]]], None, None]
                                  
+
                                  Generator[Tuple[ndarray[Any, dtype[int64]], ndarray[Any, dtype[int64]]], None, None]
                                  
 
                                  
 
                                  Returns
                                  
 
                                  a generator yielding (train, test) tuples where
diff --git a/docs/docs-version/2-1/contribution_guide.html b/docs/docs-version/2-1/contribution_guide.html
index 2a372856..46782603 100644
--- a/docs/docs-version/2-1/contribution_guide.html
+++ b/docs/docs-version/2-1/contribution_guide.html
@@ -519,7 +519,7 @@ 
                                  Sphinx Buildmake.py and conf.py. The base configuration for the
-these scripts can be found in pytools/sphinx.
+these scripts can be found in pytools/sphinx.
 The reason for this is to minimise code given the standardization of the documentation
 build across multiple packages.
                                  
 
                                  make.py: All base configuration comes from pytools/sphinx/base/make_base.py and
@@ -656,7 +656,7 @@ 
                                  Package builds
 
                                • make.py: generic Python script for package builds. Most configuration is imported
-from pytools make.py
+from pytools make.py
 which is a build script that wraps the package build, as well as exposing the matrix
 dependency definitions specified in the pyproject.toml as environment variables
                                  • 
 
                                • pyproject.toml: metadata for PyPI, build settings and package dependencies
                                  • 
diff --git a/docs/docs-version/2-1/faqs.html b/docs/docs-version/2-1/faqs.html
index 33f0c0a3..28fa0a91 100644
--- a/docs/docs-version/2-1/faqs.html
+++ b/docs/docs-version/2-1/faqs.html
@@ -209,7 +209,7 @@ 
                                  Commonly asked questionsGitHub issue tracker.
+GitHub issue tracker.
 For any other enquiries please feel free to contact us at FacetTeam@bcg.com.
                                  
 
                                  
 
                                  • 
diff --git a/docs/docs-version/2-1/objects.inv b/docs/docs-version/2-1/objects.inv
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diff --git a/docs/docs-version/2-1/tutorial/Classification_with_Facet.html b/docs/docs-version/2-1/tutorial/Classification_with_Facet.html
index 8fb6c65e..e016210b 100644
--- a/docs/docs-version/2-1/tutorial/Classification_with_Facet.html
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@@ -552,7 +552,7 @@
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-
                                  c6ed1a499dd944508790a9cc3f2b2ed1
                                  
+
                                  7e3d1c248fb143ab8d6c00cb52e68322
                                  
 
                                  
 
                                  Classification with FACET: Prediabetes Study#
                                  
 
                                  FACET is composed of the following key components:
                                  
@@ -564,7 +564,7 @@ 
                                  Classification with FACET: Prediabetes Studyscikit-learn’s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces sklearndf, an augmented version of scikit-learn with enhanced support for pandas dataframes that ensures end-to-end traceability of features.
                                  
+
                                  FACET offers an efficient and transparent machine learning workflow, enhancing scikit-learn’s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces sklearndf, an augmented version of scikit-learn with enhanced support for pandas dataframes that ensures end-to-end traceability of features.
                                  
 
 
                                
 
                                
@@ -595,7 +595,7 @@ 
                                Required imports
 
                              • Common packages (pandas, matplotlib, etc.)
                                • 
 
                              • Required FACET classes (inspection, selection, validation, simulation, etc.)
                                • 
-
                              • Other BCG GAMMA packages which simplify pipelining (sklearndf, see on GitHub) and support visualization (pytools, see on GitHub) when using FACET
                                • 
+
                              • Other BCG GAMMA packages which simplify pipelining (sklearndf, see on GitHub) and support visualization (pytools, see on GitHub) when using FACET
                                • 
 
 
                                Common package imports
                                
 
                                
@@ -629,7 +629,7 @@ 
                                Required importsGitHub). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature
+
                                Instead of using the “regular” scikit-learn package, we are going to use sklearndf (see on GitHub). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature
 traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for DF on the end. For example, the standard scikit-learn import:
                                
 
                                from sklearn.pipeline import Pipeline
                                
 
                                becomes:
                                
@@ -651,7 +651,7 @@ 
                                Required importsGitHub) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET.
                                
+
                                pytools (see on GitHub) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET.
                                
 
                                
 
                                [5]:
 
                                
@@ -1651,7 +1651,7 @@ 
                                Feature clusteringBMI, Waist Circumference and Waist/Height, we could look to eliminate one or two of these features from the model.
                                
-
                                For convenience when working in a non-notebook environment, all of the Drawer’s provided by the pytools package also support a style='text' flag.
                                
+
                                For convenience when working in a non-notebook environment, all of the Drawer’s provided by the pytools package also support a style='text' flag.
                                
 
                                
 
                                [22]:
 
                                
diff --git a/docs/docs-version/2-1/tutorial/Classification_with_Facet.ipynb b/docs/docs-version/2-1/tutorial/Classification_with_Facet.ipynb
index 633dd449..9a61e41e 100644
--- a/docs/docs-version/2-1/tutorial/Classification_with_Facet.ipynb
+++ b/docs/docs-version/2-1/tutorial/Classification_with_Facet.ipynb
@@ -31,7 +31,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
     "\n",
     "***\n",
     "\n",
@@ -108,7 +108,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, etc.)\n",
     "2. Required FACET classes (inspection, selection, validation, simulation, etc.)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-Gamma/pytools)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-X-Official/pytools)) when using FACET"
    ]
   },
   {
@@ -162,7 +162,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
+    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
@@ -194,7 +194,7 @@
    "source": [
     "**pytools imports**\n",
     "\n",
-    "pytools (see on [GitHub](https://github.com/BCG-Gamma/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
+    "pytools (see on [GitHub](https://github.com/BCG-X-Official/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
    ]
   },
   {
@@ -1557,7 +1557,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For convenience when working in a non-notebook environment, all of the `Drawer`'s provided by the [pytools](https://github.com/BCG-Gamma/pytools) package also support a `style='text'` flag."
+    "For convenience when working in a non-notebook environment, all of the `Drawer`'s provided by the [pytools](https://github.com/BCG-X-Official/pytools) package also support a `style='text'` flag."
    ]
   },
   {
diff --git a/docs/docs-version/2-1/tutorial/Model_simulation_deep_dive.html b/docs/docs-version/2-1/tutorial/Model_simulation_deep_dive.html
index 25fc78ea..8a190b23 100644
--- a/docs/docs-version/2-1/tutorial/Model_simulation_deep_dive.html
+++ b/docs/docs-version/2-1/tutorial/Model_simulation_deep_dive.html
@@ -502,7 +502,7 @@
   background: rgba(66, 165, 245, 0.2);
 }
 
-
                                e3fc61a0a5154a7b8acaa57b46da7333
                                
+
                                7e06dcb32f7f4a93b82776f6322c1df8
                                
 
                                
 
                                Work in progress#
                                
 
                                FACET 2 no longer relies on bootstrapping in simulations, and instead calculates confidence intervals based on the standard error of mean predictions. This notebook will be updated accordingly for the release of FACET 2.0.
                                
@@ -518,7 +518,7 @@ 
                                Model Simulation with FACETscikit-learn’s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces sklearndf, an augmented version of scikit-learn with enhanced support for pandas dataframes that ensures end-to-end traceability of features.
                                
+
                                FACET offers an efficient and transparent machine learning workflow, enhancing scikit-learn’s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces sklearndf, an augmented version of scikit-learn with enhanced support for pandas dataframes that ensures end-to-end traceability of features.
                                
 
                                • 
 
                              
 
                              
@@ -549,7 +549,7 @@ 
                              Required imports
 
                            • Common packages (pandas, matplotlib, etc.)
                              • 
 
                            • Required FACET classes (inspection, selection, validation, simulation, etc.)
                              • 
-
                            • Other BCG GAMMA packages which simplify pipelining (sklearndf, see on GitHub) and support visualization (pytools, see on GitHub) when using FACET
                              • 
+
                            • Other BCG GAMMA packages which simplify pipelining (sklearndf, see on GitHub) and support visualization (pytools, see on GitHub) when using FACET
                              • 
 
 
                              Common package imports
                              
 
                              
@@ -580,7 +580,7 @@ 
                              Required importsGitHub). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the
+
                              Instead of using the “regular” scikit-learn package, we are going to use sklearndf (see on GitHub). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the
 feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for DF on the end. For example, the standard scikit-learn import:
                              
 
                              from sklearn.pipeline import Pipeline
                              
 
                              becomes:
                              
@@ -595,7 +595,7 @@ 
                              Required importsGitHub) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET.
                              
+
                              pytools (see on GitHub) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET.
                              
 
                              
 
                              [5]:
 
                              
@@ -728,7 +728,7 @@ 
                              Data#<
 
                              
 
                              Bootstrap simulation overview#
                              
 
                              The figure above provides and overview of the simulation process for a single bootstrap split. This process is repeated many time to create a distribution of predicted impact for each value of a feature.
                              
-
                              ffeecc1c2f874071b52b3ccab592a2ff
                              
+
                              9273b8565aa74ef0aee353527fd98694
                              
 
                              
 
                              Partitioning#
                              
 
                              The first step to simulate feature effects on the target is to set the scope of the study for the feature we want to simulate. Let’s take the ROP measured in feet per hour (ft/h) as the feature to simulate.
                              
diff --git a/docs/docs-version/2-1/tutorial/Model_simulation_deep_dive.ipynb b/docs/docs-version/2-1/tutorial/Model_simulation_deep_dive.ipynb
index 6b10d66f..6a0d540d 100644
--- a/docs/docs-version/2-1/tutorial/Model_simulation_deep_dive.ipynb
+++ b/docs/docs-version/2-1/tutorial/Model_simulation_deep_dive.ipynb
@@ -40,7 +40,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.\n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.\n",
     "\n",
     "***\n",
     "\n",
@@ -117,7 +117,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, etc.)\n",
     "2. Required FACET classes (inspection, selection, validation, simulation, etc.)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-Gamma/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-Gamma/pytools)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-X-Official/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-X-Official/pytools)) when using FACET"
    ]
   },
   {
@@ -168,7 +168,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" *scikit-learn* package, we are going to use *sklearndf* (see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)). *sklearndf* is an open source library designed to address a common issue with *scikit-learn*: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. *sklearndf* retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard *scikit-learn* import:\n",
+    "Instead of using the \"regular\" *scikit-learn* package, we are going to use *sklearndf* (see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)). *sklearndf* is an open source library designed to address a common issue with *scikit-learn*: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. *sklearndf* retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard *scikit-learn* import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
@@ -193,7 +193,7 @@
    "source": [
     "**pytools imports**\n",
     "\n",
-    "pytools (see on [GitHub](https://github.com/BCG-Gamma/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
+    "pytools (see on [GitHub](https://github.com/BCG-X-Official/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
    ]
   },
   {
diff --git a/docs/docs-version/2-1/tutorial/Scikit-learn_classifier_summaries_using_FACET.html b/docs/docs-version/2-1/tutorial/Scikit-learn_classifier_summaries_using_FACET.html
index 216987fd..4960fd28 100644
--- a/docs/docs-version/2-1/tutorial/Scikit-learn_classifier_summaries_using_FACET.html
+++ b/docs/docs-version/2-1/tutorial/Scikit-learn_classifier_summaries_using_FACET.html
@@ -515,7 +515,7 @@
   background: rgba(66, 165, 245, 0.2);
 }
 
-
                              c6d66644177f4eaba181d8dc47acb712
                              
+
                              7d161157dca94462a0a0ca02cb42079d
                              
 
                              
 
                              Standard Scikit-learn Classification Summary with FACET#
                              
 
                              FACET is composed of the following key components:
                              
@@ -527,7 +527,7 @@ 
                              Standard Scikit-learn Classification Summary with FACETFACET’s model simulation algorithms use ML models for virtual experiments to help identify scenarios that optimise predicted outcomes. To quantify the uncertainty in simulations, FACET utilises a range of bootstrapping algorithms including stationary and stratified bootstraps. For an example of FACET’s bootstrap simulations, see the getting started example below.
                              
 
 
                            • Enhanced Machine Learning Workflow
                              
-
                              FACET offers an efficient and transparent machine learning workflow, enhancing scikit-learn’s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces sklearndf, an augmented version of scikit-learn with enhanced support for pandas dataframes that ensures end-to-end traceability of features.
                              
+
                              FACET offers an efficient and transparent machine learning workflow, enhancing scikit-learn’s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces sklearndf, an augmented version of scikit-learn with enhanced support for pandas dataframes that ensures end-to-end traceability of features.
                              
 
                              • 
 
                            
 
                            
@@ -548,7 +548,7 @@ 
                            Standard Scikit-learn Classification Summary with FACET
 
                          • Common packages (pandas, matplotlib, sklearn, etc.)
                            • 
 
                          • Required FACET classes (i.e., selection)
                            • 
-
                          • Other BCG GAMMA packages which simplify pipelining (sklearndf, see on GitHub) when using FACET
                            • 
+
                          • Other BCG GAMMA packages which simplify pipelining (sklearndf, see on GitHub) when using FACET
                            • 
 
 
                            Common package imports
                            
 
                            
@@ -590,7 +590,7 @@ 
                            Standard Scikit-learn Classification Summary with FACET
 
                            
 
                            sklearndf imports
                            
-
                            Instead of using the “regular” scikit-learn package, we are going to use sklearndf (see on GitHub). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature
+
                            Instead of using the “regular” scikit-learn package, we are going to use sklearndf (see on GitHub). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature
 traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for DF on the end. For example, the standard scikit-learn import:
                            
 
                            from sklearn.pipeline import Pipeline
                            
 
                            becomes:
                            
diff --git a/docs/docs-version/2-1/tutorial/Scikit-learn_classifier_summaries_using_FACET.ipynb b/docs/docs-version/2-1/tutorial/Scikit-learn_classifier_summaries_using_FACET.ipynb
index 00f821d3..02385b16 100644
--- a/docs/docs-version/2-1/tutorial/Scikit-learn_classifier_summaries_using_FACET.ipynb
+++ b/docs/docs-version/2-1/tutorial/Scikit-learn_classifier_summaries_using_FACET.ipynb
@@ -27,7 +27,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
     "\n",
     "***\n",
     "\n",
@@ -89,7 +89,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, sklearn, etc.)\n",
     "2. Required FACET classes (i.e., selection)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)) when using FACET"
    ]
   },
   {
@@ -151,7 +151,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
+    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
diff --git a/docs/docs-version/2-1/tutorial/Water_Drilling_Incident_Classification_with_Facet.html b/docs/docs-version/2-1/tutorial/Water_Drilling_Incident_Classification_with_Facet.html
index fa59c72e..298a2fcc 100644
--- a/docs/docs-version/2-1/tutorial/Water_Drilling_Incident_Classification_with_Facet.html
+++ b/docs/docs-version/2-1/tutorial/Water_Drilling_Incident_Classification_with_Facet.html
@@ -520,7 +520,7 @@
   background: rgba(66, 165, 245, 0.2);
 }
 
-
                            abb8bd2707be44f6a7ada70f0836d2a5
                            
+
                            41e50740c81647138c8700fd8da65851
                            
 
                            
 
                            Introduction to FACET#
                            
 
                            FACET is composed of the following key components:
                            
@@ -532,7 +532,7 @@ 
                            Introduction to FACETFACET’s model simulation algorithms use ML models for virtual experiments to help identify scenarios that optimise predicted outcomes. To quantify the uncertainty in simulations, FACET utilises a range of bootstrapping algorithms including stationary and stratified bootstraps. For an example of FACET’s bootstrap simulations, see the getting started example below.
                            
 
 
                          • Enhanced Machine Learning Workflow
                            
-
                            FACET offers an efficient and transparent machine learning workflow, enhancing scikit-learn’s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces sklearndf, an augmented version of scikit-learn with enhanced support for pandas dataframes that ensures end-to-end traceability of features.
                            
+
                            FACET offers an efficient and transparent machine learning workflow, enhancing scikit-learn’s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces sklearndf, an augmented version of scikit-learn with enhanced support for pandas dataframes that ensures end-to-end traceability of features.
                            
 
                            • 
 
                          
 
                          
@@ -560,7 +560,7 @@ 
                          Required imports
 
                        • Common packages (pandas, matplotlib, etc.)
                          • 
 
                        • Required FACET classes (inspection, selection, validation, simulation, etc.)
                          • 
-
                        • Other BCG GAMMA packages which simplify pipelining (sklearndf, see on GitHub) and support visualization (pytools, see on GitHub) when using FACET
                          • 
+
                        • Other BCG GAMMA packages which simplify pipelining (sklearndf, see on GitHub) and support visualization (pytools, see on GitHub) when using FACET
                          • 
 
 
                          Common package imports
                          
 
                          
@@ -590,8 +590,8 @@ 
                          Required importsGitHub). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability
-and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for DF on the end. For example, the standard scikit-learn import:
                          
+
                          Instead of using the “regular” scikit-learn package, we are going to use sklearndf (see on GitHub). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature
+traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for DF on the end. For example, the standard scikit-learn import:
                          
 
                          from sklearn.pipeline import Pipeline
                          
 
                          becomes:
                          
 
                          from sklearndf.pipeline import PipelineDF
                          
@@ -608,7 +608,7 @@ 
                          Required importsGitHub) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET.
                          
+
                          pytools (see on GitHub) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET.
                          
 
                          
 
                          [5]:
 
                          
diff --git a/docs/docs-version/2-1/tutorial/Water_Drilling_Incident_Classification_with_Facet.ipynb b/docs/docs-version/2-1/tutorial/Water_Drilling_Incident_Classification_with_Facet.ipynb
index 6289d3e0..5a02f025 100644
--- a/docs/docs-version/2-1/tutorial/Water_Drilling_Incident_Classification_with_Facet.ipynb
+++ b/docs/docs-version/2-1/tutorial/Water_Drilling_Incident_Classification_with_Facet.ipynb
@@ -29,7 +29,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.       \n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.       \n",
     "\n",
     "***\n",
     "\n",
@@ -111,7 +111,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, etc.)\n",
     "2. Required FACET classes (inspection, selection, validation, simulation, etc.)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-Gamma/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-Gamma/pytools)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-X-Official/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-X-Official/pytools)) when using FACET"
    ]
   },
   {
@@ -191,7 +191,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
+    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
@@ -231,7 +231,7 @@
    "source": [
     "**pytools imports**\n",
     "\n",
-    "pytools (see on [GitHub](https://github.com/BCG-Gamma/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
+    "pytools (see on [GitHub](https://github.com/BCG-X-Official/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
    ]
   },
   {
diff --git a/docs/faqs.html b/docs/faqs.html
index 33f0c0a3..28fa0a91 100644
--- a/docs/faqs.html
+++ b/docs/faqs.html
@@ -209,7 +209,7 @@ 
                          Commonly asked questionsGitHub issue tracker.
+GitHub issue tracker.
 For any other enquiries please feel free to contact us at FacetTeam@bcg.com.
                          
 
                          
 
diff --git a/docs/objects.inv b/docs/objects.inv
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diff --git a/docs/tutorial/Classification_with_Facet.html b/docs/tutorial/Classification_with_Facet.html
index 8fb6c65e..e016210b 100644
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 }
 
-
                          c6ed1a499dd944508790a9cc3f2b2ed1
                          
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                          7e3d1c248fb143ab8d6c00cb52e68322
                          
 
                          
 
                          Classification with FACET: Prediabetes Study#
                          
 
                          FACET is composed of the following key components:
                          
@@ -564,7 +564,7 @@ 
                          Classification with FACET: Prediabetes Studyscikit-learn’s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces sklearndf, an augmented version of scikit-learn with enhanced support for pandas dataframes that ensures end-to-end traceability of features.
                          
+
                          FACET offers an efficient and transparent machine learning workflow, enhancing scikit-learn’s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces sklearndf, an augmented version of scikit-learn with enhanced support for pandas dataframes that ensures end-to-end traceability of features.
                          
 
 
                        
 
                        
@@ -595,7 +595,7 @@ 
                        Required imports
 
                      • Common packages (pandas, matplotlib, etc.)
                        • 
 
                      • Required FACET classes (inspection, selection, validation, simulation, etc.)
                        • 
-
                      • Other BCG GAMMA packages which simplify pipelining (sklearndf, see on GitHub) and support visualization (pytools, see on GitHub) when using FACET
                        • 
+
                      • Other BCG GAMMA packages which simplify pipelining (sklearndf, see on GitHub) and support visualization (pytools, see on GitHub) when using FACET
                        • 
 
 
                        Common package imports
                        
 
                        
@@ -629,7 +629,7 @@ 
                        Required importsGitHub). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature
+
                        Instead of using the “regular” scikit-learn package, we are going to use sklearndf (see on GitHub). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature
 traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for DF on the end. For example, the standard scikit-learn import:
                        
 
                        from sklearn.pipeline import Pipeline
                        
 
                        becomes:
                        
@@ -651,7 +651,7 @@ 
                        Required importsGitHub) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET.
                        
+
                        pytools (see on GitHub) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET.
                        
 
                        
 
                        [5]:
 
                        
@@ -1651,7 +1651,7 @@ 
                        Feature clusteringBMI, Waist Circumference and Waist/Height, we could look to eliminate one or two of these features from the model.
                        
-
                        For convenience when working in a non-notebook environment, all of the Drawer’s provided by the pytools package also support a style='text' flag.
                        
+
                        For convenience when working in a non-notebook environment, all of the Drawer’s provided by the pytools package also support a style='text' flag.
                        
 
                        
 
                        [22]:
 
                        
diff --git a/docs/tutorial/Classification_with_Facet.ipynb b/docs/tutorial/Classification_with_Facet.ipynb
index 633dd449..9a61e41e 100644
--- a/docs/tutorial/Classification_with_Facet.ipynb
+++ b/docs/tutorial/Classification_with_Facet.ipynb
@@ -31,7 +31,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
     "\n",
     "***\n",
     "\n",
@@ -108,7 +108,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, etc.)\n",
     "2. Required FACET classes (inspection, selection, validation, simulation, etc.)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-Gamma/pytools)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-X-Official/pytools)) when using FACET"
    ]
   },
   {
@@ -162,7 +162,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
+    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
@@ -194,7 +194,7 @@
    "source": [
     "**pytools imports**\n",
     "\n",
-    "pytools (see on [GitHub](https://github.com/BCG-Gamma/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
+    "pytools (see on [GitHub](https://github.com/BCG-X-Official/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
    ]
   },
   {
@@ -1557,7 +1557,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For convenience when working in a non-notebook environment, all of the `Drawer`'s provided by the [pytools](https://github.com/BCG-Gamma/pytools) package also support a `style='text'` flag."
+    "For convenience when working in a non-notebook environment, all of the `Drawer`'s provided by the [pytools](https://github.com/BCG-X-Official/pytools) package also support a `style='text'` flag."
    ]
   },
   {
diff --git a/docs/tutorial/Model_simulation_deep_dive.html b/docs/tutorial/Model_simulation_deep_dive.html
index 25fc78ea..8a190b23 100644
--- a/docs/tutorial/Model_simulation_deep_dive.html
+++ b/docs/tutorial/Model_simulation_deep_dive.html
@@ -502,7 +502,7 @@
   background: rgba(66, 165, 245, 0.2);
 }
 
-
                        e3fc61a0a5154a7b8acaa57b46da7333
                        
+
                        7e06dcb32f7f4a93b82776f6322c1df8
                        
 
                        
 
                        Work in progress#
                        
 
                        FACET 2 no longer relies on bootstrapping in simulations, and instead calculates confidence intervals based on the standard error of mean predictions. This notebook will be updated accordingly for the release of FACET 2.0.
                        
@@ -518,7 +518,7 @@ 
                        Model Simulation with FACETscikit-learn’s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces sklearndf, an augmented version of scikit-learn with enhanced support for pandas dataframes that ensures end-to-end traceability of features.
                        
+
                        FACET offers an efficient and transparent machine learning workflow, enhancing scikit-learn’s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces sklearndf, an augmented version of scikit-learn with enhanced support for pandas dataframes that ensures end-to-end traceability of features.
                        
 
 
                      
 
                      
@@ -549,7 +549,7 @@ 
                      Required imports
 
                    • Common packages (pandas, matplotlib, etc.)
                      • 
 
                    • Required FACET classes (inspection, selection, validation, simulation, etc.)
                      • 
-
                    • Other BCG GAMMA packages which simplify pipelining (sklearndf, see on GitHub) and support visualization (pytools, see on GitHub) when using FACET
                      • 
+
                    • Other BCG GAMMA packages which simplify pipelining (sklearndf, see on GitHub) and support visualization (pytools, see on GitHub) when using FACET
                      • 
 
 
                      Common package imports
                      
 
                      
@@ -580,7 +580,7 @@ 
                      Required importsGitHub). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the
+
                      Instead of using the “regular” scikit-learn package, we are going to use sklearndf (see on GitHub). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the
 feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for DF on the end. For example, the standard scikit-learn import:
                      
 
                      from sklearn.pipeline import Pipeline
                      
 
                      becomes:
                      
@@ -595,7 +595,7 @@ 
                      Required importsGitHub) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET.
                      
+
                      pytools (see on GitHub) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET.
                      
 
                      
 
                      [5]:
 
                      
@@ -728,7 +728,7 @@ 
                      Data#<
 
                      
 
                      Bootstrap simulation overview#
                      
 
                      The figure above provides and overview of the simulation process for a single bootstrap split. This process is repeated many time to create a distribution of predicted impact for each value of a feature.
                      
-
                      ffeecc1c2f874071b52b3ccab592a2ff
                      
+
                      9273b8565aa74ef0aee353527fd98694
                      
 
                      
 
                      Partitioning#
                      
 
                      The first step to simulate feature effects on the target is to set the scope of the study for the feature we want to simulate. Let’s take the ROP measured in feet per hour (ft/h) as the feature to simulate.
                      
diff --git a/docs/tutorial/Model_simulation_deep_dive.ipynb b/docs/tutorial/Model_simulation_deep_dive.ipynb
index 6b10d66f..6a0d540d 100644
--- a/docs/tutorial/Model_simulation_deep_dive.ipynb
+++ b/docs/tutorial/Model_simulation_deep_dive.ipynb
@@ -40,7 +40,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.\n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.\n",
     "\n",
     "***\n",
     "\n",
@@ -117,7 +117,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, etc.)\n",
     "2. Required FACET classes (inspection, selection, validation, simulation, etc.)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-Gamma/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-Gamma/pytools)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-X-Official/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-X-Official/pytools)) when using FACET"
    ]
   },
   {
@@ -168,7 +168,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" *scikit-learn* package, we are going to use *sklearndf* (see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)). *sklearndf* is an open source library designed to address a common issue with *scikit-learn*: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. *sklearndf* retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard *scikit-learn* import:\n",
+    "Instead of using the \"regular\" *scikit-learn* package, we are going to use *sklearndf* (see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)). *sklearndf* is an open source library designed to address a common issue with *scikit-learn*: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. *sklearndf* retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard *scikit-learn* import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
@@ -193,7 +193,7 @@
    "source": [
     "**pytools imports**\n",
     "\n",
-    "pytools (see on [GitHub](https://github.com/BCG-Gamma/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
+    "pytools (see on [GitHub](https://github.com/BCG-X-Official/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
    ]
   },
   {
diff --git a/docs/tutorial/Scikit-learn_classifier_summaries_using_FACET.html b/docs/tutorial/Scikit-learn_classifier_summaries_using_FACET.html
index 216987fd..4960fd28 100644
--- a/docs/tutorial/Scikit-learn_classifier_summaries_using_FACET.html
+++ b/docs/tutorial/Scikit-learn_classifier_summaries_using_FACET.html
@@ -515,7 +515,7 @@
   background: rgba(66, 165, 245, 0.2);
 }
 
-
                      c6d66644177f4eaba181d8dc47acb712
                      
+
                      7d161157dca94462a0a0ca02cb42079d
                      
 
                      
 
                      Standard Scikit-learn Classification Summary with FACET#
                      
 
                      FACET is composed of the following key components:
                      
@@ -527,7 +527,7 @@ 
                      Standard Scikit-learn Classification Summary with FACETFACET’s model simulation algorithms use ML models for virtual experiments to help identify scenarios that optimise predicted outcomes. To quantify the uncertainty in simulations, FACET utilises a range of bootstrapping algorithms including stationary and stratified bootstraps. For an example of FACET’s bootstrap simulations, see the getting started example below.
                      
 
                      • 
 
                    • Enhanced Machine Learning Workflow
                      
-
                      FACET offers an efficient and transparent machine learning workflow, enhancing scikit-learn’s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces sklearndf, an augmented version of scikit-learn with enhanced support for pandas dataframes that ensures end-to-end traceability of features.
                      
+
                      FACET offers an efficient and transparent machine learning workflow, enhancing scikit-learn’s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces sklearndf, an augmented version of scikit-learn with enhanced support for pandas dataframes that ensures end-to-end traceability of features.
                      
 
                      • 
 
                    
 
                    
@@ -548,7 +548,7 @@ 
                    Standard Scikit-learn Classification Summary with FACET
 
                  • Common packages (pandas, matplotlib, sklearn, etc.)
                    • 
 
                  • Required FACET classes (i.e., selection)
                    • 
-
                  • Other BCG GAMMA packages which simplify pipelining (sklearndf, see on GitHub) when using FACET
                    • 
+
                  • Other BCG GAMMA packages which simplify pipelining (sklearndf, see on GitHub) when using FACET
                    • 
 
 
                    Common package imports
                    
 
                    
@@ -590,7 +590,7 @@ 
                    Standard Scikit-learn Classification Summary with FACET
 
                    
 
                    sklearndf imports
                    
-
                    Instead of using the “regular” scikit-learn package, we are going to use sklearndf (see on GitHub). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature
+
                    Instead of using the “regular” scikit-learn package, we are going to use sklearndf (see on GitHub). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature
 traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for DF on the end. For example, the standard scikit-learn import:
                    
 
                    from sklearn.pipeline import Pipeline
                    
 
                    becomes:
                    
diff --git a/docs/tutorial/Scikit-learn_classifier_summaries_using_FACET.ipynb b/docs/tutorial/Scikit-learn_classifier_summaries_using_FACET.ipynb
index 00f821d3..02385b16 100644
--- a/docs/tutorial/Scikit-learn_classifier_summaries_using_FACET.ipynb
+++ b/docs/tutorial/Scikit-learn_classifier_summaries_using_FACET.ipynb
@@ -27,7 +27,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
     "\n",
     "***\n",
     "\n",
@@ -89,7 +89,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, sklearn, etc.)\n",
     "2. Required FACET classes (i.e., selection)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)) when using FACET"
    ]
   },
   {
@@ -151,7 +151,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
+    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
diff --git a/docs/tutorial/Water_Drilling_Incident_Classification_with_Facet.html b/docs/tutorial/Water_Drilling_Incident_Classification_with_Facet.html
index fa59c72e..298a2fcc 100644
--- a/docs/tutorial/Water_Drilling_Incident_Classification_with_Facet.html
+++ b/docs/tutorial/Water_Drilling_Incident_Classification_with_Facet.html
@@ -520,7 +520,7 @@
   background: rgba(66, 165, 245, 0.2);
 }
 
-
                    abb8bd2707be44f6a7ada70f0836d2a5
                    
+
                    41e50740c81647138c8700fd8da65851
                    
 
                    
 
                    Introduction to FACET#
                    
 
                    FACET is composed of the following key components:
                    
@@ -532,7 +532,7 @@ 
                    Introduction to FACETFACET’s model simulation algorithms use ML models for virtual experiments to help identify scenarios that optimise predicted outcomes. To quantify the uncertainty in simulations, FACET utilises a range of bootstrapping algorithms including stationary and stratified bootstraps. For an example of FACET’s bootstrap simulations, see the getting started example below.
                    
 
 
                  • Enhanced Machine Learning Workflow
                    
-
                    FACET offers an efficient and transparent machine learning workflow, enhancing scikit-learn’s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces sklearndf, an augmented version of scikit-learn with enhanced support for pandas dataframes that ensures end-to-end traceability of features.
                    
+
                    FACET offers an efficient and transparent machine learning workflow, enhancing scikit-learn’s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces sklearndf, an augmented version of scikit-learn with enhanced support for pandas dataframes that ensures end-to-end traceability of features.
                    
 
                    • 
 
                  
 
                  
@@ -560,7 +560,7 @@ 
                  Required imports
 
                • Common packages (pandas, matplotlib, etc.)
                  • 
 
                • Required FACET classes (inspection, selection, validation, simulation, etc.)
                  • 
-
                • Other BCG GAMMA packages which simplify pipelining (sklearndf, see on GitHub) and support visualization (pytools, see on GitHub) when using FACET
                  • 
+
                • Other BCG GAMMA packages which simplify pipelining (sklearndf, see on GitHub) and support visualization (pytools, see on GitHub) when using FACET
                  • 
 
 
                  Common package imports
                  
 
                  
@@ -590,8 +590,8 @@ 
                  Required importsGitHub). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability
-and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for DF on the end. For example, the standard scikit-learn import:
                  
+
                  Instead of using the “regular” scikit-learn package, we are going to use sklearndf (see on GitHub). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature
+traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for DF on the end. For example, the standard scikit-learn import:
                  
 
                  from sklearn.pipeline import Pipeline
                  
 
                  becomes:
                  
 
                  from sklearndf.pipeline import PipelineDF
                  
@@ -608,7 +608,7 @@ 
                  Required importsGitHub) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET.
                  
+
                  pytools (see on GitHub) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET.
                  
 
                  
 
                  [5]:
 
                  
diff --git a/docs/tutorial/Water_Drilling_Incident_Classification_with_Facet.ipynb b/docs/tutorial/Water_Drilling_Incident_Classification_with_Facet.ipynb
index 6289d3e0..5a02f025 100644
--- a/docs/tutorial/Water_Drilling_Incident_Classification_with_Facet.ipynb
+++ b/docs/tutorial/Water_Drilling_Incident_Classification_with_Facet.ipynb
@@ -29,7 +29,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.       \n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.       \n",
     "\n",
     "***\n",
     "\n",
@@ -111,7 +111,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, etc.)\n",
     "2. Required FACET classes (inspection, selection, validation, simulation, etc.)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-Gamma/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-Gamma/pytools)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-X-Official/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-X-Official/pytools)) when using FACET"
    ]
   },
   {
@@ -191,7 +191,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
+    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
@@ -231,7 +231,7 @@
    "source": [
     "**pytools imports**\n",
     "\n",
-    "pytools (see on [GitHub](https://github.com/BCG-Gamma/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
+    "pytools (see on [GitHub](https://github.com/BCG-X-Official/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
    ]
   },
   {