Class SelectFpr<T>

Namespace

AiDotNet.Preprocessing.FeatureSelection

Assembly

AiDotNet.dll

Selects features based on a false positive rate test.

public class SelectFpr<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>>

SelectFpr<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

SelectFpr selects features whose p-value is below a threshold (alpha). This controls the expected percentage of false positives among all features.

For example, with alpha=0.05, we expect about 5% of the selected features to be false positives (features that passed the test by chance).

For Beginners: This selector uses statistical significance: - Computes a p-value for each feature (how likely it's just noise) - Keeps features with p-value below alpha (threshold) - Lower alpha = stricter selection = fewer false positives

Constructors

SelectFpr(double, SelectKBestScoreFunc, int[]?)

Creates a new instance of SelectFpr<T>.

public SelectFpr(double alpha = 0.05, SelectKBestScoreFunc scoringFunction = SelectKBestScoreFunc.FRegression, int[]? columnIndices = null)

Parameters

alpha double

Significance level for feature selection. Defaults to 0.05.

scoringFunction SelectKBestScoreFunc

The scoring function to use. Defaults to FRegression.

columnIndices int[]

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

Properties

Alpha

Gets the significance level (alpha).

public double Alpha { get; }

Property Value

double

PValues

Gets the p-values for each feature.

public double[]? PValues { get; }

Property Value

double[]

Scores

Gets the scores for each feature.

public double[]? Scores { get; }

Property Value

double[]

ScoringFunction

Gets the scoring function used.

public SelectKBestScoreFunc ScoringFunction { get; }

Property Value

SelectKBestScoreFunc

SelectedIndices

Gets the indices of selected features.

public int[]? SelectedIndices { 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 p-values.

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 significant features.

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

Parameters

data Matrix<T>

Returns

Matrix<T>