RandomForestRegressor

Algorithm overview

RandomForestRegressor averages predictions from many decision trees. It is useful for nonlinear numeric prediction when a single regression tree is too noisy.

JavaScript implementation

@kanaries/ml exposes Ensemble.RandomForestRegressor for JavaScript and TypeScript projects. It supports bootstrap sampling, feature subsampling, and seeded randomness.

Quick start example

import { Ensemble } from '@kanaries/ml';

const reg = new Ensemble.RandomForestRegressor({
  nEstimators: 25,
  maxDepth: 4,
  randomState: 7,
});

reg.fit([[0], [1], [2], [3]], [0, 1, 4, 9]);
const pred = reg.predict([[4]]);

Detailed API reference

new Ensemble.RandomForestRegressor(props?: {
  nEstimators?: number;
  bootstrap?: boolean;
  maxDepth?: number;
  minSamplesSplit?: number;
  maxFeatures?: number | 'sqrt' | 'log2';
  randomState?: number;
})

Methods:

• fit(trainX: number[][], trainY: number[]): void
• predict(testX: number[][]): number[]

Defaults are nEstimators: 100, bootstrap: true, and maxFeatures: 1.0.