Table of Contents

Class ExtraTreesClassifier<T>

Namespace
AiDotNet.Classification.Ensemble
Assembly
AiDotNet.dll

Extra Trees (Extremely Randomized Trees) classifier.

public class ExtraTreesClassifier<T> : EnsembleClassifierBase<T>, ITreeBasedClassifier<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 (e.g., float, double).

Inheritance
ExtraTreesClassifier<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

Extra Trees is an ensemble method that builds multiple decision trees with extra randomization. Unlike Random Forest which finds the best split among random features, Extra Trees picks random splits, leading to more diversity.

For Beginners: Extra Trees takes randomization even further than Random Forest:

Random Forest: "Look at random features, pick the BEST split" Extra Trees: "Look at random features, pick a RANDOM split"

Benefits of Extra Trees:

  • Faster training (no need to find optimal splits)
  • Often better generalization
  • More robust to noise

When Extra Trees might be better:

  • When you have noisy data
  • When Random Forest overfits
  • When you need faster training

Constructors

ExtraTreesClassifier(ExtraTreesClassifierOptions<T>?, IRegularization<T, Matrix<T>, Vector<T>>?)

Initializes a new instance of the ExtraTreesClassifier class.

public ExtraTreesClassifier(ExtraTreesClassifierOptions<T>? options = null, IRegularization<T, Matrix<T>, Vector<T>>? regularization = null)

Parameters

options ExtraTreesClassifierOptions<T>

Configuration options for Extra Trees.

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

Optional regularization strategy.

Properties

LeafCount

Gets the number of leaf nodes in the tree.

public int LeafCount { get; }

Property Value

int

The count of terminal nodes (leaves) in the trained tree. Returns 0 if the model has not been trained.

MaxDepth

Gets the maximum depth of the tree.

public int MaxDepth { get; }

Property Value

int

The maximum depth reached during training, or the configured maximum depth.

NodeCount

Gets the number of internal (decision) nodes in the tree.

public int NodeCount { get; }

Property Value

int

The count of non-terminal nodes that make decisions. Returns 0 if the model has not been trained.

Options

Gets the Extra Trees specific options.

protected ExtraTreesClassifierOptions<T> Options { get; }

Property Value

ExtraTreesClassifierOptions<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

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

Trains the Extra Trees classifier on the provided data.

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

Parameters

x Matrix<T>
y Vector<T>
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