import { ClassifierBase } from '../base';
import { Params } from '../base/estimator';
import { SubsetSizeOption } from '../utils/paramResolvers';
import { DecisionTreeProps } from '../tree';
export interface BaggingClassifierProps extends DecisionTreeProps {
    nEstimators?: number;
    /** positive integer = absolute count; fraction in (0,1) = share of samples; 'all'/undefined = all */
    maxSamples?: SubsetSizeOption;
    bootstrap?: boolean;
    randomState?: number;
    estimatorFactory?: (seed?: number) => {
        fit(X: number[][], y: number[]): void;
        predict(X: number[][]): number[];
    };
}
export declare class BaggingClassifier extends ClassifierBase {
    private nEstimators;
    private maxSamples?;
    private bootstrap;
    private randomState?;
    private estimatorFactory?;
    private treeProps;
    private estimators;
    private fitted;
    constructor(props?: BaggingClassifierProps);
    /**
     * NOTE: `estimatorFactory` is a function-valued param; it is returned
     * as-is (clone/setParams keep working), but an instance constructed with
     * a custom factory cannot be serialized with toJSON().
     */
    getParams(): Params;
    private defaultEstimator;
    fit(trainX: number[][], trainY: number[]): void;
    predict(testX: number[][]): number[];
}