---
title: "SelectFromModel, RFE, and RFECV in JavaScript"
description: "Run model-based and recursive feature selection in JavaScript or TypeScript with SelectFromModel, RFE, and RFECV from @kanaries/ml."
canonical_url: "https://ml.kanaries.net/docs/apis/feature_selection/selectors"
markdown_url: "https://ml.kanaries.net/docs/apis/feature_selection/selectors.md"
---
# Model-based feature selection in JavaScript

## Algorithm overview

`SelectFromModel` keeps features above an importance threshold. `RFE` repeatedly removes the least important features. `RFECV` evaluates RFE feature counts with cross-validation and chooses the best count. All three require an estimator that exposes `featureImportances` or `coef`.

## JavaScript implementation

The `FeatureSelection` namespace brings these sklearn-style meta-estimators to browser and Node.js workflows. Each selector clones its estimator, so the supplied prototype remains unfitted.

## Quick start example

```ts
import { FeatureSelection, Tree } from '@kanaries/ml';

const X = [[0, 1, 9], [1, 0, 9], [8, 1, 9], [9, 0, 9]];
const y = [0, 0, 1, 1];
const selector = new FeatureSelection.RFE({
  estimator: new Tree.DecisionTreeClassifier({ randomState: 42 }),
  nFeaturesToSelect: 1,
});
selector.fit(X, y);
console.log(selector.getSupport(true), selector.transform(X));
```

## Detailed API reference

- `SelectFromModel({ estimator, threshold?, maxFeatures? })`: threshold accepts a number, `mean`, `median`, `k*mean`, or `k*median`. Exposes `fit`, `transform`, `getSupport`, and `fittedEstimator`.
- `RFE({ estimator, nFeaturesToSelect?, step? })`: counts may be integers or fractions. Exposes `fit`, `transform`, `predict`, `score`, `getSupport`, and `ranking`.
- `RFECV({ estimator, minFeaturesToSelect?, step?, cv?, scoring? })`: classifier defaults use stratified folds. It additionally exposes `gridScores` and `cvResults` with `nFeatures` and `meanTestScore`.