Regression

All regressors implement IRegressionModel.

📈 Linear

Class: Linear

Hyperparameter	Values
LearningRate	Step size
FitIntercept	Include bias term

🔷 Ridge Regression (L2)

Class: Ridge

Hyperparameter	Values
Alpha	Regularization strength
FitIntercept	Include bias term

✂️ Lasso Regression (L1)

Class: Lasso

Hyperparameter	Values
Alpha	Regularization strength
MaxIterations	Convergence limit

🔗 Elastic Net (L1 + L2)

Class: ElasticNet

Hyperparameter	Values
Lambda	Regularization strength
L1Ratio	L1 vs L2 balance

➡️ Support Vector Regression (Linear)

Class: LinearSVR

Hyperparameter	Values
C	Regularization strength
Epsilon	Insensitive zone
LearningRate	Step size
Epochs	Training iterations

🎯 Support Vector Regression (Kernel)

Class: KernelSVR

Hyperparameter	Values
C	Regularization strength
LearningRate	Step size
Epochs	Training iterations
Kernel	RBF, Polynomial
Gamma	Kernel coefficient
Degree	Polynomial degree

🧠 Multilayer Perceptron (Regressor)

Class: MLPRegressor

Hyperparameter	Values
HiddenLayers	e.g. 64, 64,32
LearningRate	Step size
Epochs	Training iterations
BatchSize	Mini-batch size
L2	L2 regularization
Activation	ReLU, Tanh, Sigmoid