Class SelectKBest<T>

Namespace

AiDotNet.Preprocessing.FeatureSelection

Assembly

AiDotNet.dll

Selects the K highest-scoring features according to a scoring function.

public class SelectKBest<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>>

SelectKBest<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

SelectKBest computes a score for each feature based on the relationship between the feature and the target variable, then selects the top K features with the highest scores.

Built-in scoring functions include: - F-score for regression (linear relationship) - Mutual information (any relationship type)

For Beginners: Not all features are equally useful for prediction. SelectKBest helps you: - Reduce the number of features to improve model speed - Remove noisy features that might hurt model accuracy - Find the most informative features for understanding your problem

Example: From 100 features, select the 10 most related to your target.

Constructors

SelectKBest(int, SelectKBestScoreFunc, int[]?)

Creates a new instance of SelectKBest<T>.

public SelectKBest(int k = 10, SelectKBestScoreFunc scoreFunc = SelectKBestScoreFunc.FRegression, int[]? columnIndices = null)

Parameters

k int

Number of features to select. Defaults to 10.

scoreFunc SelectKBestScoreFunc

Scoring function to use. Defaults to FRegression.

columnIndices int[]

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

Properties

K

Gets the number of features to select.

public int K { get; }

Property Value

int

PValues

Gets the p-values for each feature (if applicable).

public double[]? PValues { get; }

Property Value

double[]

ScoreFunc

Gets the scoring function used.

public SelectKBestScoreFunc ScoreFunc { get; }

Property Value

SelectKBestScoreFunc

Scores

Gets the computed scores for each feature.

public double[]? Scores { get; }

Property Value

double[]

SelectedFeatures

Gets the indices of selected features.

public int[]? SelectedFeatures { 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 the selector by computing feature scores based on the target.

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

Parameters

data Matrix<T>

The feature matrix to fit.

target Vector<T>

The target values.

FitCore(Matrix<T>)

Fits the selector using variance-based scoring when no target is provided.

protected override void FitCore(Matrix<T> data)

Parameters

data Matrix<T>

Remarks

When called without target values, this method falls back to variance-based feature selection. Features with higher variance are scored higher. For supervised selection, use Fit(Matrix<T> data, Vector<T> target) instead.

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

Fits the selector and transforms the data in one step.

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 a boolean 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>)

Selects the top K features from the data.

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

Parameters

data Matrix<T>

Returns

Matrix<T>