import { Scikit1D, Scikit2D, Tensor1D, Tensor2D } from '../types';
import { ClassifierMixin } from '../mixins';
export interface VotingClassifierParams {
    /** 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[];
    /** If ‘hard’, uses predicted class labels for majority rule voting.
     * Else if ‘soft’, predicts the class label based on the argmax of the
     *  sums of the predicted probabilities, which is recommended for an
     * ensemble of well-calibrated classifiers.
     */
    voting?: 'hard' | 'soft';
}
/**
 * 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 { VotingClassifier, DummyClassifier, LogisticRegression } from 'scikitjs'
 *
 * const X = [
      [1, 2],
      [2, 1],
      [2, 2],
      [3, 1],
      [4, 4]
    ]
    const y = [0, 0, 1, 1, 1]
    const voter = new VotingClassifier({
      estimators: [
        ['dt', new DummyClassifier()],
        ['dt', new DummyClassifier()],
        ['lr', new LogisticRegression({ penalty: 'none' })]
      ]
    })

    await voter.fit(X, y)
    assert.deepEqual(voter.predict(X).arraySync(), [1, 1, 1, 1, 1])
 * ```
 */
export declare class VotingClassifier extends ClassifierMixin {
    estimators: Array<[string, any]>;
    weights?: number[];
    le: any;
    name: string;
    tf: any;
    constructor({ estimators, weights, voting }?: VotingClassifierParams);
    fit(X: Scikit2D, y: Scikit1D): Promise<VotingClassifier>;
    predictProba(X: Scikit2D): Tensor1D;
    predict(X: Scikit2D): Tensor1D;
    transform(X: Scikit2D): Array<Tensor1D> | Array<Tensor2D>;
    fitTransform(X: Scikit2D, y: Scikit1D): Promise<Array<Tensor1D> | Array<Tensor2D>>;
}
export declare function makeVotingClassifier(...args: any[]): VotingClassifier;