import { BaseEstimator, Params } from '../base/estimator';
import { SingleOutputEstimator } from './common';
export interface MultiOutputRegressorProps {
    /** Prototype regressor; one unfitted clone is fitted per output column. */
    estimator: BaseEstimator;
}
/**
 * Multi-target regression wrapper, mirroring sklearn's
 * `MultiOutputRegressor`: one clone of `estimator` is fitted per output
 * column of the 2-D target matrix `Y` [nSamples][nOutputs].
 *
 * `fit(X, Y)` takes a 2-D target, so this extends `BaseEstimator` directly
 * rather than `RegressorBase` (whose contract is 1-D y).
 *
 * `score(X, Y)` is the **uniform-average R²** over the outputs: R² is
 * computed per output column and the unweighted mean is returned — sklearn's
 * `MultiOutputRegressor.score` (`r2_score` with
 * `multioutput='uniform_average'`).
 *
 * Nested params are addressable as `estimator__<param>` in `setParams`.
 */
export declare class MultiOutputRegressor extends BaseEstimator {
    private estimator;
    private estimators_;
    constructor(props: MultiOutputRegressorProps);
    getParams(): Params;
    /** Supports own params plus nested `estimator__param` addressing. */
    setParams(params: Params): this;
    /** The fitted per-output members, ordered like Y's columns. */
    get estimators(): SingleOutputEstimator[];
    fit(X: number[][], Y: number[][], sampleWeight?: number[]): void;
    private ensureFitted;
    /** Predicted target matrix, shape [nSamples][nOutputs]. */
    predict(X: number[][]): number[][];
    /**
     * Uniform-average R² over the outputs (mean of per-column R²), matching
     * sklearn's `MultiOutputRegressor.score`.
     */
    score(X: number[][], Y: number[][]): number;
}