Ensemble.AdaBoostClassifier

interface AdaBoostClassifierProps {
    nEstimators?: number;
    learningRate?: number;
    randomState?: number;
}
constructor(props: AdaBoostClassifierProps = {})

Parameters

Algorithm

AdaBoostClassifier trains decision stumps sequentially and reweights samples so that misclassified points receive more focus in subsequent rounds.

Methods

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

Example

const clf = new AdaBoostClassifier();
clf.fit(trainX, trainY);
const result = clf.predict(testX);

Practical guide: AdaBoostClassifier in JavaScript and TypeScript

AdaBoostClassifier focuses on hard examples across rounds to improve classification performance over weak learners.

When to use AdaBoostClassifier

• Baseline classifiers miss difficult boundary regions.
• You need improved recall/precision without switching to heavy models.
• You can invest in hyperparameter tuning for learning rate and rounds.

Implementation workflow

1. Start with balanced preprocessing and clear label quality checks.
2. Fit with several estimator counts and compare classification metrics.
3. Tune threshold and class-weight strategy for product-specific tradeoffs.

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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