import { Scikit1D, Scikit2D, Tensor1D } from '../types';
import { RegressorMixin } from '../mixins';
export interface VotingRegressorParams {
    /** List of name, estimator pairs. Example
     * `[['lr', new LinearRegression()], ['dt', new DecisionTree()]]`
     */
    estimators?: Array<[string, any]>;
    /** The weights for the estimators. If not present, then there is a uniform weighting. */
    weights?: number[];
}
/**
 * A voting regressor is an ensemble meta-estimator that fits several base
 * regressors, each on the whole dataset. Then it averages the individual
 * predictions to form a final prediction.
 *
 * @example
 * ```js
 * import { VotingRegressor, DecisionTreeRegressor, LinearRegression } from 'scikitjs'
 *
 * const X = [
      [2, 2],
      [2, 3],
      [5, 4],
      [1, 0]
    ]
    const y = [5, 3, 4, 1.5]
    const voter = new VotingRegressor({
      estimators: [
        ['dt', new DecisionTreeRegressor()],
        ['lr', new LinearRegression({ fitIntercept: false })]
      ]
    })

    await voter.fit(X, y)
 * ```
 */
export declare class VotingRegressor extends RegressorMixin {
    estimators: Array<[string, any]>;
    weights?: number[];
    name: string;
    constructor({ estimators, weights }?: VotingRegressorParams);
    fit(X: Scikit2D, y: Scikit1D): Promise<VotingRegressor>;
    predict(X: Scikit2D): Tensor1D;
    transform(X: Scikit2D): Array<Tensor1D>;
    fitTransform(X: Scikit2D, y: Scikit1D): Promise<Tensor1D[]>;
}
/**
 *
 * Helper function for make a VotingRegressor. Just pass your Estimators as function arguments.
 *
 * @example
 * ```typescript
 * import {makeVotingRegressor, DummyRegressor, LinearRegression} from 'scikitjs'
 *  const X = [
      [1, 2],
      [2, 1],
      [2, 2],
      [3, 1]
    ]
    const y = [3, 3, 4, 4]
    const voter = makeVotingRegressor(
      new DummyRegressor(),
      new LinearRegression({ fitIntercept: true })
    )

    await voter.fit(X, y)
    ```
 */
export declare function makeVotingRegressor(...args: any[]): VotingRegressor;