Interface IMultiLabelClassifier<T>

Namespace

AiDotNet.Interfaces

Assembly

AiDotNet.dll

Interface for multi-label classifiers that can predict multiple labels per sample.

public interface 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.

Inherited Members

IClassifier<T>.NumClasses

IClassifier<T>.TaskType

IClassifier<T>.ClassLabels

IFullModel<T, Matrix<T>, Vector<T>>.DefaultLossFunction

IModel<Matrix<T>, Vector<T>, ModelMetadata<T>>.Train(Matrix<T>, Vector<T>)

IModel<Matrix<T>, Vector<T>, ModelMetadata<T>>.Predict(Matrix<T>)

IModel<Matrix<T>, Vector<T>, ModelMetadata<T>>.GetModelMetadata()

IModelSerializer.Serialize()

IModelSerializer.Deserialize(byte[])

IModelSerializer.SaveModel(string)

IModelSerializer.LoadModel(string)

ICheckpointableModel.SaveState(Stream)

ICheckpointableModel.LoadState(Stream)

IParameterizable<T, Matrix<T>, Vector<T>>.GetParameters()

IParameterizable<T, Matrix<T>, Vector<T>>.SetParameters(Vector<T>)

IParameterizable<T, Matrix<T>, Vector<T>>.ParameterCount

IParameterizable<T, Matrix<T>, Vector<T>>.WithParameters(Vector<T>)

IFeatureAware.GetActiveFeatureIndices()

IFeatureAware.SetActiveFeatureIndices(IEnumerable<int>)

IFeatureAware.IsFeatureUsed(int)

IFeatureImportance<T>.GetFeatureImportance()

ICloneable<IFullModel<T, Matrix<T>, Vector<T>>>.DeepCopy()

ICloneable<IFullModel<T, Matrix<T>, Vector<T>>>.Clone()

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

IGradientComputable<T, Matrix<T>, Vector<T>>.ApplyGradients(Vector<T>, T)

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

IJitCompilable<T>.SupportsJitCompilation

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

Multi-label classification differs from multi-class classification: - Multi-class: One label per sample (mutually exclusive) - Multi-label: Zero, one, or many labels per sample (not mutually exclusive)

For Beginners: In multi-label classification, each sample can have multiple labels:

Examples:

• An article tagged with "politics", "economy", and "international"
• A movie classified as "action", "comedy", and "romance"
• An image containing "dog", "person", and "outdoor"

The output is a binary matrix where each column is a label indicator.

Properties

NumLabels

Gets the number of labels that can be predicted.

int NumLabels { get; }

Property Value

int

Methods

PredictMultiLabel(Matrix<T>)

Predicts binary indicators for each label for each sample.

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.

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.