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Overview

SelectKBest selects the K highest scoring features according to a scoring function. Commonly used scoring functions include chi2, f_classif, and mutual_info_classif.

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Constructor

new SelectKBest(scoreFunc, options?)

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Parameters

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scoreFunc
Function
required
Scoring function that takes (X, y) and returns scores and p-values. Common choices: chi2, f_classif, f_regression, mutualInfoClassif.
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options.k
number | 'all'
default:"10"
Number of top features to select. Use 'all' to keep all features.

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Methods

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fit()

fit(X: Matrix, y: Vector): this
Run score function on (X, y) and select features.
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X
Matrix
required
Training data matrix.
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y
Vector
required
Target values.

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transform()

transform(X: Matrix): Matrix
Reduce X to selected features.
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X
Matrix
required
Data to transform.

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fitTransform()

fitTransform(X: Matrix, y: Vector): Matrix
Fit and transform in one step.

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Properties

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scores_
number[] | null
Scores of all features.
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pvalues_
number[] | null
P-values of feature scores (if supported by scoring function).
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selectedFeatureIndices_
number[] | null
Indices of selected features.

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Examples

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Classification with chi2

import { SelectKBest, chi2, LogisticRegression } from "bun-scikit";

const selector = new SelectKBest(chi2, { k: 5 });
const XNew = selector.fitTransform(XTrain, yTrain);

console.log("Selected features:", selector.selectedFeatureIndices_);
console.log("Feature scores:", selector.scores_);

const model = new LogisticRegression();
model.fit(XNew, yTrain);

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Regression with f_regression

import { SelectKBest, f_regression, LinearRegression } from "bun-scikit";

const selector = new SelectKBest(f_regression, { k: 3 });
const XTrain_selected = selector.fitTransform(XTrain, yTrain);
const XTest_selected = selector.transform(XTest);

const model = new LinearRegression();
model.fit(XTrain_selected, yTrain);
const predictions = model.predict(XTest_selected);

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Use in pipeline

import { Pipeline, SelectKBest, f_classif, StandardScaler, SVC } from "bun-scikit";

const pipeline = new Pipeline([
  ["feature_selection", new SelectKBest(f_classif, { k: 20 })],
  ["scaler", new StandardScaler()],
  ["svm", new SVC({ kernel: "rbf" })]
]);

pipeline.fit(XTrain, yTrain);
const score = pipeline.score(XTest, yTest);

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Notes

  • Univariate feature selection examines each feature independently
  • Does not account for feature interactions
  • Fast and scalable to high-dimensional datasets
  • chi2 requires non-negative features (e.g., count data, TF-IDF)
  • f_classif and f_regression are suitable for continuous features

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