Class MultiOutputClassifier<T>

Namespace

AiDotNet.Classification.Meta

Assembly

AiDotNet.dll

Multi-output classifier for independent multi-label classification.

public class MultiOutputClassifier<T> : MetaClassifierBase<T>, IProbabilisticClassifier<T>, IMultiLabelClassifier<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

object

ClassifierBase<T>

ProbabilisticClassifierBase<T>

MetaClassifierBase<T>

MultiOutputClassifier<T>

Implements

IProbabilisticClassifier<T>

IMultiLabelClassifier<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>

Inherited Members

MetaClassifierBase<T>.Options

MetaClassifierBase<T>.EstimatorFactory

MetaClassifierBase<T>.CreateBaseEstimator()

MetaClassifierBase<T>.GetParameters()

MetaClassifierBase<T>.SetParameters(Vector<T>)

MetaClassifierBase<T>.WithParameters(Vector<T>)

MetaClassifierBase<T>.ComputeGradients(Matrix<T>, Vector<T>, ILossFunction<T>)

MetaClassifierBase<T>.ApplyGradients(Vector<T>, T)

ProbabilisticClassifierBase<T>.ApplySoftmax(Matrix<T>)

ProbabilisticClassifierBase<T>.ApplySigmoid(Vector<T>)

ProbabilisticClassifierBase<T>.GetModelMetadata()

ClassifierBase<T>.NumOps

ClassifierBase<T>.Engine

ClassifierBase<T>.Regularization

ClassifierBase<T>.NumClasses

ClassifierBase<T>.TaskType

ClassifierBase<T>.ClassLabels

ClassifierBase<T>.NumFeatures

ClassifierBase<T>.FeatureNames

ClassifierBase<T>.ExpectedParameterCount

ClassifierBase<T>.InferTaskType(Vector<T>)

ClassifierBase<T>.ExtractClassLabels(Vector<T>)

ClassifierBase<T>.ComputeClassWeights(Vector<T>)

ClassifierBase<T>.GetClassIndexFromLabel(T)

ClassifierBase<T>.Serialize()

ClassifierBase<T>.Deserialize(byte[])

ClassifierBase<T>.GetActiveFeatureIndices()

ClassifierBase<T>.IsFeatureUsed(int)

ClassifierBase<T>.SetActiveFeatureIndices(IEnumerable<int>)

ClassifierBase<T>.GetFeatureImportance()

ClassifierBase<T>.DeepCopy()

ClassifierBase<T>.ParameterCount

ClassifierBase<T>.DefaultLossFunction

ClassifierBase<T>.SaveModel(string)

ClassifierBase<T>.LoadModel(string)

ClassifierBase<T>.SaveState(Stream)

ClassifierBase<T>.LoadState(Stream)

ClassifierBase<T>.SupportsJitCompilation

ClassifierBase<T>.ExportComputationGraph(List<ComputationNode<T>>)

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Extension Methods

DistributedExtensions.AsDistributedForHighBandwidth<T, TInput, TOutput>(IFullModel<T, TInput, TOutput>, ICommunicationBackend<T>)

DistributedExtensions.AsDistributedForLowBandwidth<T, TInput, TOutput>(IFullModel<T, TInput, TOutput>, ICommunicationBackend<T>)

DistributedExtensions.AsDistributed<T, TInput, TOutput>(IFullModel<T, TInput, TOutput>, ICommunicationBackend<T>)

DistributedExtensions.AsDistributed<T, TInput, TOutput>(IFullModel<T, TInput, TOutput>, IShardingConfiguration<T>)

Remarks

MultiOutputClassifier fits one classifier per target label, treating each label as independent of the others.

For Beginners: MultiOutputClassifier is the simplest multi-label approach:

For labels A, B, C:

• Classifier 1: Predict A using features X
• Classifier 2: Predict B using features X
• Classifier 3: Predict C using features X

Each classifier is completely independent.

When to use:

• When labels are truly independent
• As a simple baseline for multi-label problems
• When you don't need to model label correlations

Note: Unlike ClassifierChain, this does NOT capture label dependencies.

Constructors

MultiOutputClassifier(Func<IClassifier<T>>, MetaClassifierOptions<T>?, IRegularization<T, Matrix<T>, Vector<T>>?)

Initializes a new instance of the MultiOutputClassifier class.

public MultiOutputClassifier(Func<IClassifier<T>> estimatorFactory, MetaClassifierOptions<T>? options = null, IRegularization<T, Matrix<T>, Vector<T>>? regularization = null)

Parameters

estimatorFactory Func<IClassifier<T>>

Factory function to create base classifiers.

options MetaClassifierOptions<T>

Configuration options for the classifier.

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

Optional regularization strategy.

Properties

NumLabels

Gets the number of labels that can be predicted.

public int NumLabels { get; }

Property Value

int

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.

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.

PredictMultiLabel(Matrix<T>)

Predicts binary indicators for each label for each sample.

public Matrix<T> PredictMultiLabel(Matrix<T> input)

Parameters

input Matrix<T>

The input feature matrix.

Returns

Matrix<T>

A binary matrix where each row is a sample and each column is a label indicator (1=present, 0=absent).

PredictMultiLabelProbabilities(Matrix<T>)

Predicts probabilities for each label for each sample.

public Matrix<T> PredictMultiLabelProbabilities(Matrix<T> input)

Parameters

input Matrix<T>

The input feature matrix.

Returns

Matrix<T>

A probability matrix where each row is a sample and each column is the probability of that label.

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>)

Standard training method - converts single labels to multi-label format.

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

Parameters

x Matrix<T>

y Vector<T>

TrainMultiLabel(Matrix<T>, Matrix<T>)

Trains the Multi-output classifier on multi-label data.

public void TrainMultiLabel(Matrix<T> x, Matrix<T> yMultiLabel)

Parameters

x Matrix<T>

The input features matrix.

yMultiLabel Matrix<T>

The multi-label target matrix (rows=samples, cols=labels).