BaggingClassifier
Train bootstrap classifier ensembles with the BaggingClassifier JavaScript and TypeScript implementation in @kanaries/ml.
View as MarkdownAlgorithm 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]]);
console.log(pred);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.
BaggingRegressor
Reduce regression variance with bootstrap aggregation using the BaggingRegressor JavaScript and TypeScript implementation in @kanaries/ml.