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Classification

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

📊 Logistic Regression​

Class: Logistic

Hyperparameters:

  • LearningRate
  • MaxIterations
  • FitIntercept
  • RegularizationStrength
  • Tolerance

🌳 Decision Tree​

Class: DecisionTree

Hyperparameters:

  • MaxDepth
  • MinSamplesSplit

🌲 Random Forest​

Class: RandomForest

Hyperparameters:

  • NumTrees
  • MaxDepth
  • MinSamplesSplit

👥 K-Nearest Neighbors​

Class: KNearestNeighbors

Hyperparameters:

  • K

🎲 Naive Bayes​

Class: NaiveBayes

Hyperparameters: (No tunable hyperparameters)

➡️ Support Vector Classifier (Linear)​

Class: LinearSVC

Hyperparameters:

  • C (regularization strength)
  • LearningRate
  • Epochs

🎯 Support Vector Classifier (Kernel)​

Class: KernelSVC

Hyperparameters:

  • C
  • Kernel (RBF, Polynomial)
  • LearningRate
  • Epochs
  • Gamma
  • Degree (for polynomial kernel)

🧠 Multilayer Perceptron (Classifier)​

Class: MLPClassifier

Hyperparameters:

  • HiddenLayers (e.g. 64, 64,32)
  • LearningRate
  • Epochs
  • Activation (ReLU, Tanh, Sigmoid)