BaggingClassifier
Train bootstrap classifier ensembles with the BaggingClassifier JavaScript and TypeScript implementation in @kanaries/ml.
Algorithm overview
BaggingClassifier trains multiple classifiers on resampled datasets and predicts by vote. It is useful for reducing variance in unstable base estimators such as decision trees.
JavaScript implementation
@kanaries/ml implements Ensemble.BaggingClassifier with a default decision-tree base estimator. You can also pass an estimatorFactory for custom estimators that implement fit and predict.
Quick start example
import { Ensemble } from '@kanaries/ml';
const clf = new Ensemble.BaggingClassifier({
nEstimators: 20,
maxSamples: 3,
randomState: 12,
});
clf.fit([[0, 0], [0, 1], [3, 3], [4, 3]], [0, 0, 1, 1]);
const pred = clf.predict([[1, 1], [4, 4]]);Detailed API reference
new Ensemble.BaggingClassifier(props?: {
nEstimators?: number;
maxSamples?: number;
bootstrap?: boolean;
randomState?: number;
estimatorFactory?: (seed?: number) => {
fit(X: number[][], y: number[]): void;
predict(X: number[][]): number[];
};
// plus DecisionTreeClassifier options when using the default estimator
})Methods:
fit(trainX: number[][], trainY: number[]): voidpredict(testX: number[][]): number[]
Defaults are nEstimators: 10 and bootstrap: true.
RandomForestRegressor
Predict continuous targets with tree ensembles using the RandomForestRegressor JavaScript and TypeScript implementation in @kanaries/ml.
AdaBoost Classifier
Learn what AdaBoost Classifier does, when to use it, and how to run AdaBoostClassifier in JavaScript or TypeScript with @kanaries/ml for browser and Node.js applications.