Ensemble.AdaBoostRegressor

constructor(props?: { estimator?: DecisionTreeRegressor; n_estimators?: number; learning_rate?: number })

Parameters

Algorithm

AdaBoostRegressor combines weak regressors sequentially. Each new estimator focuses on samples that previous models predicted poorly.

Methods

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

Example

const regr = new AdaBoostRegressor({ n_estimators: 100 });
regr.fit(X, y);
const pred = regr.predict([[0, 0, 0, 0]]);

Practical guide: AdaBoostRegressor in JavaScript and TypeScript

AdaBoostRegressor combines weak regressors sequentially to improve predictive accuracy on non-linear regression patterns.

When to use AdaBoostRegressor

• Single linear models underfit important non-linear behavior.
• You need stronger regression quality with manageable model complexity.
• Your deployment environment requires fast JS inference.

Implementation workflow

1. Train with a conservative learning rate and enough estimators.
2. Validate MAE/RMSE on holdout data and monitor overfitting.
3. Tune estimator count and learning rate for stability vs accuracy.

JavaScript deployment notes

• Prefer feature scaling for distance-based and gradient-based algorithms to improve stability.
• In browser apps, run heavy training in Web Workers to keep UI interactions smooth.
• Keep a simple baseline from the same module as a fallback model for comparison.

Search intents this page targets

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