Table of Contents

Class VotingClassifier<T>

Namespace
AiDotNet.Classification.Meta
Assembly
AiDotNet.dll

Voting classifier that combines multiple classifiers through voting.

public class VotingClassifier<T> : MetaClassifierBase<T>, IProbabilisticClassifier<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.

Inheritance
VotingClassifier<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

Voting classifier combines predictions from multiple different classifiers using either hard voting (majority vote) or soft voting (average probabilities).

For Beginners: Voting combines predictions from multiple models:

Hard Voting:

  • Each classifier votes for a class
  • The class with most votes wins
  • Example: [A, A, B] -> A wins (2 vs 1)

Soft Voting:

  • Average the probability predictions
  • Pick the class with highest average probability
  • Generally works better when classifiers output calibrated probabilities

When to use:

  • To combine different types of classifiers
  • When you want to reduce the risk of a single bad model
  • To leverage the strengths of different algorithms

Constructors

VotingClassifier(IEnumerable<IClassifier<T>>, VotingClassifierOptions<T>?, IRegularization<T, Matrix<T>, Vector<T>>?)

Initializes a new instance of the VotingClassifier class.

public VotingClassifier(IEnumerable<IClassifier<T>> estimators, VotingClassifierOptions<T>? options = null, IRegularization<T, Matrix<T>, Vector<T>>? regularization = null)

Parameters

estimators IEnumerable<IClassifier<T>>

List of classifiers to combine.

options VotingClassifierOptions<T>

Configuration options for the classifier.

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

Optional regularization strategy.

Properties

Options

Gets the voting-specific options.

protected VotingClassifierOptions<T> Options { get; }

Property Value

VotingClassifierOptions<T>

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.

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).

PredictLogProbabilities(Matrix<T>)

Predicts log-probabilities for each class.

public override Matrix<T> PredictLogProbabilities(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 are the natural logarithm of the class probabilities.

Remarks

The default implementation computes log(PredictProbabilities(input)). Subclasses that compute log-probabilities directly (like Naive Bayes) should override this method for better numerical stability.

For Beginners: Log-probabilities are probabilities transformed by the natural logarithm. They're useful for numerical stability when working with very small probabilities.

For example:

  • Probability 0.9 → Log-probability -0.105
  • Probability 0.1 → Log-probability -2.303
  • Probability 0.001 → Log-probability -6.908

Log-probabilities are always negative (since probabilities are between 0 and 1). Higher (less negative) values mean higher probability.

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 all classifiers in the voting ensemble.

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

Parameters

x Matrix<T>
y Vector<T>
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