From ce8acb48932ab5f166aec25866249282c070b843 Mon Sep 17 00:00:00 2001 From: Piotrek Date: Tue, 9 Apr 2019 15:48:34 +0200 Subject: [PATCH] minor fixes --- README.md | 15 ++++++++++++--- setup.py | 2 +- 2 files changed, 13 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 8a4bc26b..36cd96d3 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ Thanks to Automated Machine Learning you don't need to worry about different mac In the current version only binary classification is supported with optimization of LogLoss metric. -## Example +## Quick example ```python import pandas as pd @@ -28,12 +28,21 @@ automl = AutoML() automl.fit(X, y) predictions = automl.predict(X) - - ``` ## The tuning algorithm +The tuning algorithm was created and developed by Piotr Płoński. It is heuristic algorithm created from combination of: + +- **not-so-random** approach +- and **hill-climbing** + +The approach is **not-so-random** because each algorithm has a defined set of hyper-parameters that usually works. At first step from not so random parameters an initial set of models is drawn. Then the hill climbing approach is used to pick best performing algorithms and tune them. + +For each algorithm used in the AutoML the early stopping is applied. + +The ensemble algorithm was implemented based on [Caruana paper](http://www.cs.cornell.edu/~alexn/papers/shotgun.icml04.revised.rev2.pdf). + ## Installation From PyPi repository: diff --git a/setup.py b/setup.py index 12c15e56..784e8ff9 100644 --- a/setup.py +++ b/setup.py @@ -10,7 +10,7 @@ setup( name='mljar-supervised', - version='0.1.0', + version='0.1.1', description='Automated Machine Learning for Supervised tasks', long_description=long_description, long_description_content_type="text/markdown",