Class RFE<T>

Namespace

AiDotNet.Preprocessing.FeatureSelection

Assembly

AiDotNet.dll

Recursive Feature Elimination for feature selection.

public class RFE<T> : TransformerBase<T, Matrix<T>, Matrix<T>>, IDataTransformer<T, Matrix<T>, Matrix<T>>

Type Parameters

T

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

Inheritance

object

TransformerBase<T, Matrix<T>, Matrix<T>>

RFE<T>

Implements

IDataTransformer<T, Matrix<T>, Matrix<T>>

Inherited Members

TransformerBase<T, Matrix<T>, Matrix<T>>.NumOps

TransformerBase<T, Matrix<T>, Matrix<T>>.Engine

TransformerBase<T, Matrix<T>, Matrix<T>>.IsFitted

TransformerBase<T, Matrix<T>, Matrix<T>>.ColumnIndices

TransformerBase<T, Matrix<T>, Matrix<T>>.SupportsInverseTransform

TransformerBase<T, Matrix<T>, Matrix<T>>.Fit(Matrix<T>)

TransformerBase<T, Matrix<T>, Matrix<T>>.Transform(Matrix<T>)

TransformerBase<T, Matrix<T>, Matrix<T>>.FitTransform(Matrix<T>)

TransformerBase<T, Matrix<T>, Matrix<T>>.InverseTransform(Matrix<T>)

TransformerBase<T, Matrix<T>, Matrix<T>>.GetFeatureNamesOut(string[])

TransformerBase<T, Matrix<T>, Matrix<T>>.FitCore(Matrix<T>)

TransformerBase<T, Matrix<T>, Matrix<T>>.TransformCore(Matrix<T>)

TransformerBase<T, Matrix<T>, Matrix<T>>.InverseTransformCore(Matrix<T>)

TransformerBase<T, Matrix<T>, Matrix<T>>.ValidateInputData(Matrix<T>)

TransformerBase<T, Matrix<T>, Matrix<T>>.EnsureFitted()

TransformerBase<T, Matrix<T>, Matrix<T>>.GetColumnsToProcess(int)

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Remarks

RFE performs feature selection by recursively removing features and building a model on the remaining features. It ranks features by importance and removes the least important ones until the desired number of features is reached.

The algorithm: 1. Train a model on all features and compute feature importances 2. Remove the least important feature(s) 3. Repeat until desired number of features is reached

For Beginners: RFE is like an elimination tournament for features: - Start with all features - Remove the weakest performer each round - Keep going until you have the desired number of features - The surviving features are the most important ones

Constructors

RFE(int, int, RFEImportanceMethod, int[]?)

Creates a new instance of RFE<T>.

public RFE(int nFeaturesToSelect = 5, int step = 1, RFEImportanceMethod importanceMethod = RFEImportanceMethod.Correlation, int[]? columnIndices = null)

Parameters

nFeaturesToSelect int

Number of features to select. Defaults to 5.

step int

Number of features to remove at each iteration. Defaults to 1.

importanceMethod RFEImportanceMethod

Method for computing feature importance. Defaults to Correlation.

columnIndices int[]

The column indices to evaluate, or null for all columns.

Properties

FeatureImportances

Gets the feature importances from the final model.

public double[]? FeatureImportances { get; }

Property Value

double[]

NFeaturesToSelect

Gets the number of features to select.

public int NFeaturesToSelect { get; }

Property Value

int

Ranking

Gets the feature ranking (1 = selected, 2+ = elimination order).

public int[]? Ranking { get; }

Property Value

int[]

SelectedIndices

Gets the indices of selected features.

public int[]? SelectedIndices { get; }

Property Value

int[]

Step

Gets the step size (features removed per iteration).

public int Step { get; }

Property Value

int

SupportsInverseTransform

Gets whether this transformer supports inverse transformation.

public override bool SupportsInverseTransform { get; }

Property Value

bool

Methods

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

Fits RFE by recursively eliminating features.

public void Fit(Matrix<T> data, Vector<T> target)

Parameters

data Matrix<T>

The feature matrix.

target Vector<T>

The target values.

FitCore(Matrix<T>)

Fits the selector (requires target via specialized Fit method).

protected override void FitCore(Matrix<T> data)

Parameters

data Matrix<T>

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

Fits and transforms the data.

public Matrix<T> FitTransform(Matrix<T> data, Vector<T> target)

Parameters

data Matrix<T>

target Vector<T>

Returns

Matrix<T>

GetFeatureNamesOut(string[]?)

Gets the output feature names after transformation.

public override string[] GetFeatureNamesOut(string[]? inputFeatureNames = null)

Parameters

inputFeatureNames string[]

Returns

string[]

GetSupportMask()

Gets the support mask indicating which features are selected.

public bool[] GetSupportMask()

Returns

bool[]

InverseTransformCore(Matrix<T>)

Inverse transformation is not supported.

protected override Matrix<T> InverseTransformCore(Matrix<T> data)

Parameters

data Matrix<T>

Returns

Matrix<T>

TransformCore(Matrix<T>)

Transforms the data by selecting the most important features.

protected override Matrix<T> TransformCore(Matrix<T> data)

Parameters

data Matrix<T>

Returns

Matrix<T>