diff --git a/_ml-commons-plugin/algorithms.md b/_ml-commons-plugin/algorithms.md
index d7809d51b2..a5a173b358 100644
--- a/_ml-commons-plugin/algorithms.md
+++ b/_ml-commons-plugin/algorithms.md
@@ -59,20 +59,31 @@ The training process supports multithreading, but the number of threads must be
 
 ## Linear regression
 
-Linear regression maps the linear relationship between inputs and outputs. In ML Commons, the linear regression algorithm is adopted from the public machine learning library [Tribuo](https://tribuo.org/), which offers multidimensional linear regression models. The model supports the linear optimizer in training, including popular approaches like Linear Decay, SQRT_DECAY, [ADA](https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf), [ADAM](https://tribuo.org/learn/4.1/javadoc/org/tribuo/math/optimisers/Adam.html), and [RMS_DROP](https://tribuo.org/learn/4.1/javadoc/org/tribuo/math/optimisers/RMSProp.html). 
+Linear regression maps the linear relationship between inputs and outputs. In ML Commons, the linear regression algorithm is adopted from the public machine learning library [Tribuo](https://tribuo.org/), which offers multidimensional linear regression models. The model supports the linear optimizer in training, including popular approaches like Linear Decay, SQRT_DECAY, [ADA](https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf), [ADAM](https://tribuo.org/learn/4.2/javadoc/org/tribuo/math/optimisers/Adam.html), and [RMS_PROP](https://tribuo.org/learn/4.2/javadoc/org/tribuo/math/optimisers/RMSProp.html).
+
+**Optimizers supported:** [SIMPLE_SGD](https://tribuo.org/learn/4.2/javadoc/org/tribuo/math/optimisers/SGD.html#:~:text=learning%20rate%20SGD.-,getSimpleSGD,-public%20static%C2%A0), [LINEAR_DECAY_SGD](https://tribuo.org/learn/4.2/javadoc/org/tribuo/math/optimisers/SGD.html#:~:text=linear%20decay%20SGD.-,getLinearDecaySGD,-public%20static%C2%A0), [SQRT_DECAY_SGD](https://tribuo.org/learn/4.2/javadoc/org/tribuo/math/optimisers/SGD.html#:~:text=sqrt%20decay%20SGD.-,getSqrtDecaySGD,-public%20static%C2%A0), [ADA_GRAD](https://tribuo.org/learn/4.2/javadoc/org/tribuo/math/optimisers/AdaGrad.html), [ADA_DELTA](https://tribuo.org/learn/4.1/javadoc/org/tribuo/math/optimisers/AdaDelta.html), [ADAM](https://tribuo.org/learn/4.1/javadoc/org/tribuo/math/optimisers/Adam.html), and [RMS_PROP](https://tribuo.org/learn/4.1/javadoc/org/tribuo/math/optimisers/RMSProp.html).  
+**Objectives supported:** [ABSOLUTE_LOSS](https://tribuo.org/learn/4.2/javadoc/org/tribuo/regression/sgd/objectives/AbsoluteLoss.html), [HUBER](https://tribuo.org/learn/4.2/javadoc/org/tribuo/regression/sgd/objectives/Huber.html), and [SQUARED_LOSS](https://tribuo.org/learn/4.2/javadoc/org/tribuo/regression/sgd/objectives/SquaredLoss.html).  
+**momentum_type supported:** [STANDARD](https://tribuo.org/learn/4.2/javadoc/org/tribuo/math/optimisers/SGD.Momentum.html#STANDARD:~:text=No%20momentum.-,STANDARD,-public%20static%20final) and [NESTEROV](https://tribuo.org/learn/4.2/javadoc/org/tribuo/math/optimisers/SGD.Momentum.html#STANDARD:~:text=Standard%20momentum.-,NESTEROV,-public%20static%20final).  
 
 ### Parameters
 
 Parameter | Type   | Description | Default value
 :--- |:--- | :--- | :---
-`learningRate` | Double | The initial step size used in an iterative optimization algorithm. | `0.01`
-`momentumFactor` | Double | The extra weight factors that accelerate the rate at which the weight is adjusted. This helps move the minimization routine out of local minima.  | `0`
+`target` | String | The name of the target variable to predict. Identifies which feature the model will learn to predict during training. | `NA`
+`learning_rate` | Double | The initial step size used in an iterative optimization algorithm. | `0.01`
+`momentum_factor` | Double | The extra weight factors that accelerate the rate at which the weight is adjusted. This helps move the minimization routine out of local minima.  | `0`
 `epsilon` | Double | The value for stabilizing gradient inversion. | `1.00E-06`
 `beta1` | Double | The exponential decay rates for the moment estimates. |  `0.9`
 `beta2` | Double | The exponential decay rates for the moment estimates. |  `0.99`
-`decayRate` | Double | The Root Mean Squared Propagation (RMSProp). | `0.9`
-`momentumType` | String | The defined Stochastic Gradient Descent (SGD) momentum type that helps accelerate gradient vectors in the right directions, leading to a fast convergence.| `STANDARD`
-`optimizerType` | String | The optimizer used in the model. | `SIMPLE_SGD`
+`decay_rate` | Double | The Root Mean Squared Propagation (RMSProp). | `0.9`
+`momentum_type` | String | The defined Stochastic Gradient Descent (SGD) momentum type that helps accelerate gradient vectors in the right directions, leading to a fast convergence.| `STANDARD`
+`optimiser` | String | The optimizer used in the model. | `SIMPLE_SGD`
+`objective` | String | The objective function used. | `SQUARED_LOSS` 
+`epochs` | Integer | The number of iterations. | `5`|
+`batch_size` | Integer | The minimum batch size. | `1`
+`logging_interval` | Integer | The frequency of logging during training iterations. Set to `-1` to disable logging. | `-1`
+`seed` | Long | A random seed used for reproducible results. Controls the initialization of random number generators. | `12345`
+
 
 
 ### Supported APIs
@@ -412,23 +423,27 @@ The Localization algorithm can only be executed directly. Therefore, it cannot b
 
 A classification algorithm, logistic regression models the probability of a discrete outcome given an input variable. In ML Commons, these classifications include both binary and multi-class. The most common is the binary classification, which takes two values, such as "true/false" or "yes/no", and predicts the outcome based on the values specified. Alternatively, a multi-class output can categorize different inputs based on type. This makes logistic regression most useful for situations where you are trying to determine how your inputs fit best into a specified category. 
 
+**Optimizers supported:** [SIMPLE_SGD](https://tribuo.org/learn/4.2/javadoc/org/tribuo/math/optimisers/SGD.html#:~:text=learning%20rate%20SGD.-,getSimpleSGD,-public%20static%C2%A0), [LINEAR_DECAY_SGD](https://tribuo.org/learn/4.2/javadoc/org/tribuo/math/optimisers/SGD.html#:~:text=linear%20decay%20SGD.-,getLinearDecaySGD,-public%20static%C2%A0), [SQRT_DECAY_SGD](https://tribuo.org/learn/4.2/javadoc/org/tribuo/math/optimisers/SGD.html#:~:text=sqrt%20decay%20SGD.-,getSqrtDecaySGD,-public%20static%C2%A0), [ADA_GRAD](https://tribuo.org/learn/4.2/javadoc/org/tribuo/math/optimisers/AdaGrad.html), [ADA_DELTA](https://tribuo.org/learn/4.1/javadoc/org/tribuo/math/optimisers/AdaDelta.html), [ADAM](https://tribuo.org/learn/4.1/javadoc/org/tribuo/math/optimisers/Adam.html), and [RMS_PROP](https://tribuo.org/learn/4.1/javadoc/org/tribuo/math/optimisers/RMSProp.html).
+**Objectives supported:** [HINGE](https://tribuo.org/learn/4.2/javadoc/org/tribuo/classification/sgd/objectives/Hinge.html) and [LOGMULTICLASS](https://tribuo.org/learn/4.2/javadoc/org/tribuo/classification/sgd/objectives/LogMulticlass.html).  
+**Momentum type supported:** [STANDARD](https://tribuo.org/learn/4.2/javadoc/org/tribuo/math/optimisers/SGD.Momentum.html#STANDARD:~:text=No%20momentum.-,STANDARD,-public%20static%20final) and [NESTEROV](https://tribuo.org/learn/4.2/javadoc/org/tribuo/math/optimisers/SGD.Momentum.html#STANDARD:~:text=Standard%20momentum.-,NESTEROV,-public%20static%20final).  
+
 ### Parameters
 
 | Parameter | Type | Description | Default value |
 |---|---|---|---|
-| `learningRate` | Double | The initial step size used in an iterative optimization algorithm. | `1` |
-| `momentumFactor` | Double | The extra weight factors that accelerate the rate at which the weight is adjusted. This helps move the minimization routine out of local minima. | `0` |
+| `learning_rate` | Double | The initial step size used in an iterative optimization algorithm. | `1` |
+| `momentum_factor` | Double | The extra weight factors that accelerate the rate at which the weight is adjusted. This helps move the minimization routine out of local minima. | `0` |
 | `epsilon` | Double | The value for stabilizing gradient inversion. | `0.1` |
 | `beta1` | Double | The exponential decay rates for the moment estimates. | `0.9` |
 | `beta2` | Double | The exponential decay rates for the moment estimates. | `0.99` |
-| `decayRate` | Double | The Root Mean Squared Propagation (RMSProp). | `0.9` |
-| `momentumType` | String | The Stochastic Gradient Descent (SGD) momentum that helps accelerate gradient vectors in the right direction, leading to faster convergence between vectors. | `STANDARD` |
-| `optimizerType` | String | The optimizer used in the model.  | `AdaGrad` |
+| `decay_rate` | Double | The Root Mean Squared Propagation (RMSProp). | `0.9` |
+| `momentum_type` | String | The Stochastic Gradient Descent (SGD) momentum that helps accelerate gradient vectors in the right directions, leading to a fast convergence. | `STANDARD` |
+| `optimiser` | String | The optimizer used in the model.  | `ADA_GRAD` |
 | `target` | String | The target field. | null |
-| `objectiveType` | String | The objective function type. | `LogMulticlass` |
+| `objective` | String | The objective function type. | `LOGMULTICLASS` |
 | `epochs` | Integer | The number of iterations. | `5` |
-| `batchSize` | Integer | The size of min batches. | `1` |
-| `loggingInterval` | Integer | The interval of logs lost after many iterations. The interval is `1` if the algorithm contains no logs. | `1000` |
+| `batch_size` | Integer | The minimum batch size. | `1` |
+| `logging_interval` | Integer | The interval of logs lost after many iterations. The interval is `1` if the algorithm contains no logs. | `1000` |
 
 ### Supported APIs