Table of Contents

Class LinearSupportVectorClassifier<T>

Namespace
AiDotNet.Classification.SVM
Assembly
AiDotNet.dll

Linear Support Vector Classifier optimized for linear classification.

public class LinearSupportVectorClassifier<T> : SVMBase<T>, IProbabilisticClassifier<T>, IDecisionFunctionClassifier<T>, IClassifier<T>, IFullModel<T, Matrix<T>, Vector<T>>, IModel<Matrix<T>, Vector<T>, ModelMetadata<T>>, IModelSerializer, ICheckpointableModel, IParameterizable<T, Matrix<T>, Vector<T>>, IFeatureAware, IFeatureImportance<T>, ICloneable<IFullModel<T, Matrix<T>, Vector<T>>>, IGradientComputable<T, Matrix<T>, Vector<T>>, IJitCompilable<T>

Type Parameters

T

The numeric data type used for calculations (e.g., float, double).

Inheritance
LinearSupportVectorClassifier<T>
Implements
IFullModel<T, Matrix<T>, Vector<T>>
IModel<Matrix<T>, Vector<T>, ModelMetadata<T>>
IParameterizable<T, Matrix<T>, Vector<T>>
ICloneable<IFullModel<T, Matrix<T>, Vector<T>>>
IGradientComputable<T, Matrix<T>, Vector<T>>
Inherited Members
Extension Methods

Remarks

This implementation uses a primal formulation with stochastic gradient descent (SGD) for efficient training on large datasets. Unlike the standard SVC which uses the kernel trick, this classifier works directly in the original feature space.

For Beginners: Linear SVC is a simplified version of SVM that only draws straight lines to separate classes. It's much faster to train than the regular SVC because it doesn't need to compute kernel values between all pairs of training points.

Use Linear SVC when:

  • You have a large dataset (thousands of samples)
  • Your data is linearly separable or nearly so
  • You need fast training and prediction
  • You have high-dimensional data (many features)

Example use cases:

  • Text classification (spam detection, sentiment)
  • Document categorization
  • High-dimensional bioinformatics data

Constructors

LinearSupportVectorClassifier(SVMOptions<T>?, IRegularization<T, Matrix<T>, Vector<T>>?)

Initializes a new instance of the LinearSupportVectorClassifier class.

public LinearSupportVectorClassifier(SVMOptions<T>? options = null, IRegularization<T, Matrix<T>, Vector<T>>? regularization = null)

Parameters

options SVMOptions<T>

Configuration options for the Linear SVC.

regularization IRegularization<T, Matrix<T>, Vector<T>>

Optional regularization strategy.

Methods

Clone()

Creates a clone of the classifier model.

public override IFullModel<T, Matrix<T>, Vector<T>> Clone()

Returns

IFullModel<T, Matrix<T>, Vector<T>>

A new instance of the model with the same parameters and options.

CreateNewInstance()

Creates a new instance of the same type as this classifier.

protected override IFullModel<T, Matrix<T>, Vector<T>> CreateNewInstance()

Returns

IFullModel<T, Matrix<T>, Vector<T>>

A new instance of the same classifier type.

DecisionFunction(Matrix<T>)

Computes the decision function for the input samples.

public override Matrix<T> DecisionFunction(Matrix<T> input)

Parameters

input Matrix<T>

The input features matrix where each row is a sample.

Returns

Matrix<T>

A matrix of decision values. For binary classification, this is a single column representing the signed distance to the decision boundary. For multi-class, the shape depends on the multi-class strategy (OvR vs OvO).

Remarks

The decision function provides the "raw" output of the classifier before any probability calibration. For SVMs, this is the signed distance to the separating hyperplane.

For Beginners: This gives you the classifier's "confidence" without converting to probabilities.

Use this when you want to:

  • Apply custom thresholds for classification
  • Understand how confident the classifier is
  • Create your own probability calibration

GetModelMetadata()

Gets metadata about the model.

public override ModelMetadata<T> GetModelMetadata()

Returns

ModelMetadata<T>

A ModelMetadata object containing information about the model.

Remarks

This method returns metadata about the model, including its type, feature count, complexity, description, and additional information specific to classification.

For Beginners: Model metadata provides information about the model itself, rather than the predictions it makes. This includes details about the model's structure (like how many features it uses) and characteristics (like how many classes it can predict). This information can be useful for understanding and comparing different models.

GetModelType()

Returns the model type identifier for this classifier.

protected override ModelType GetModelType()

Returns

ModelType

Predict(Matrix<T>)

Predicts class labels for the given input data by taking the argmax of probabilities.

public override Vector<T> Predict(Matrix<T> input)

Parameters

input Matrix<T>

The input features matrix where each row is an example and each column is a feature.

Returns

Vector<T>

A vector of predicted class indices for each input example.

Remarks

This implementation uses the argmax of the probability distribution to determine the predicted class. For binary classification with a custom decision threshold, you may want to use PredictProbabilities() directly and apply your own threshold.

For Beginners: This method picks the class with the highest probability for each sample.

For example, if the probabilities are [0.1, 0.7, 0.2] for classes [A, B, C], this method returns class B (index 1) because it has the highest probability (0.7).

PredictProbabilities(Matrix<T>)

Predicts class probabilities for each sample in the input.

public override Matrix<T> PredictProbabilities(Matrix<T> input)

Parameters

input Matrix<T>

The input features matrix where each row is a sample and each column is a feature.

Returns

Matrix<T>

A matrix where each row corresponds to an input sample and each column corresponds to a class. The values represent the probability of the sample belonging to each class.

Remarks

This abstract method must be implemented by derived classes to compute class probabilities. The output matrix should have shape [num_samples, num_classes], and each row should sum to 1.0.

For Beginners: This method computes the probability of each sample belonging to each class. Each row in the output represents one sample, and each column represents one class. The values in each row sum to 1.0 (100% total probability).

Train(Matrix<T>, Vector<T>)

Trains the Linear SVC on the provided data using SGD.

public override void Train(Matrix<T> x, Vector<T> y)

Parameters

x Matrix<T>
y Vector<T>
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