Classification

All classifiers implement IClassificationModel and operate directly on Matrix and Vector primitives.

📊 Logistic Regression

Class: Logistic

Hyperparameter	Values
LearningRate	Step size
MaxIterations	Convergence limit
FitIntercept	Include bias term
RegularizationStrength	L2 penalty
Tolerance	Convergence threshold

🌳 Decision Tree

Class: DecisionTree

Hyperparameter	Values
MaxDepth	Maximum tree depth
MinSamplesSplit	Minimum samples to split

🌲 Random Forest

Class: RandomForest

Hyperparameter	Values
NumTrees	Number of trees
MaxDepth	Maximum tree depth
MinSamplesSplit	Minimum samples to split

👥 K-Nearest Neighbors

Class: KNearestNeighbors

Hyperparameter	Values
K	Number of neighbors

🎲 Naive Bayes

Class: NaiveBayes

No tunable hyperparameters.

➡️ Support Vector Classifier (Linear)

Class: LinearSVC

Hyperparameter	Values
C	Regularization strength
LearningRate	Step size
Epochs	Training iterations

🎯 Support Vector Classifier (Kernel)

Class: KernelSVC

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

🧠 Multilayer Perceptron (Classifier)

Class: MLPClassifier

Hyperparameter	Values
HiddenLayers	e.g. 64, 64,32
LearningRate	Step size
Epochs	Training iterations
Activation	ReLU, Tanh, Sigmoid