import { BaseEstimator, Params } from '../base/estimator';
export interface KFoldProps {
    nSplits?: number;
    shuffle?: boolean;
    randomState?: number;
}
export interface FoldIndices {
    trainIndices: number[];
    testIndices: number[];
}
export interface EstimatorLike {
    fit(trainX: number[][], trainY: number[]): void;
    predict(testX: number[][]): number[];
    score?: (X: number[][], Y: number[]) => number;
}
/** sklearn-style classifier detection, including Pipeline final estimators. */
export declare function isClassifierLike(estimator: unknown): boolean;
export interface CrossValScoreOptions {
    cv?: number | SplitterLike;
    scoring?: (actual: number[], expected: number[]) => number;
}
export interface SplitterLike {
    /**
     * `groups` is only consumed by group-aware splitters (e.g. `GroupKFold`);
     * all other splitters ignore it.
     */
    split(X: any[], y?: any[], groups?: any[]): FoldIndices[];
}
export interface SearchEstimatorFactory {
    (params: Record<string, any>): EstimatorLike;
}
export type ScoringFunction = (actual: number[], expected: number[]) => number;
/** A contract-following estimator usable as a search prototype. */
export type SearchEstimator = BaseEstimator & EstimatorLike;
/**
 * Built-in scoring functions addressable by name in the `scoring` param so
 * that search meta-estimators remain serializable. All are
 * higher-is-better (losses are negated).
 */
export declare const SCORING_FUNCS: Record<string, ScoringFunction>;
export declare function resolveScoring(scoring: ScoringFunction | string | undefined): ScoringFunction | undefined;
export interface GridSearchCVProps {
    /**
     * Prototype estimator; each candidate is `estimator.clone().setParams(p)`.
     * Serializable (the estimator is stored as a registered class instance).
     * Exactly one of `estimator` / `estimatorFactory` must be provided.
     */
    estimator?: SearchEstimator;
    /** Factory building an estimator from params. NOT serializable. */
    estimatorFactory?: SearchEstimatorFactory;
    paramGrid: Record<string, any[]>;
    cv?: number | SplitterLike;
    /**
     * Either a scoring function or the string name of a built-in
     * (e.g. 'accuracyScore'). Only string names are serializable.
     */
    scoring?: ScoringFunction | string;
    refit?: boolean;
}
export interface RandomizedSearchCVProps {
    /** See GridSearchCVProps.estimator. */
    estimator?: SearchEstimator;
    /** See GridSearchCVProps.estimatorFactory. */
    estimatorFactory?: SearchEstimatorFactory;
    paramDistributions: Record<string, any[]>;
    nIter: number;
    cv?: number | SplitterLike;
    scoring?: ScoringFunction | string;
    randomState?: number;
    refit?: boolean;
}
export declare class KFold {
    private nSplits;
    private shuffle;
    private randomState?;
    constructor(props?: KFoldProps);
    split(X: any[], y?: any[]): FoldIndices[];
}
export declare class StratifiedKFold implements SplitterLike {
    private nSplits;
    private shuffle;
    private randomState?;
    constructor(props?: KFoldProps);
    split(X: any[], y?: any[]): FoldIndices[];
}
export declare class GridSearchCV extends BaseEstimator {
    private estimator?;
    private estimatorFactory?;
    private paramGrid;
    private cv?;
    private scoring?;
    private refit;
    bestParams: Record<string, any> | null;
    bestScore: number;
    bestEstimator: EstimatorLike | null;
    constructor(props: GridSearchCVProps);
    getParams(): Params;
    private buildEstimator;
    fit(X: number[][], y: number[]): void;
    predict(X: number[][]): number[];
    score(X: number[][], y: number[]): number;
}
export declare class RandomizedSearchCV extends BaseEstimator {
    private estimator?;
    private estimatorFactory?;
    private paramDistributions;
    private nIter;
    private cv?;
    private scoring?;
    private randomState?;
    private refit;
    bestParams: Record<string, any> | null;
    bestScore: number;
    bestEstimator: EstimatorLike | null;
    constructor(props: RandomizedSearchCVProps);
    getParams(): Params;
    private buildEstimator;
    fit(X: number[][], y: number[]): void;
    predict(X: number[][]): number[];
    score(X: number[][], y: number[]): number;
}
export declare function crossValScore(estimatorFactory: () => EstimatorLike, X: number[][], y: number[], options?: CrossValScoreOptions): number[];
/** Out-of-fold predictions, restored to the original sample order. */
export declare function crossValPredict(estimatorFactory: () => EstimatorLike, X: number[][], y: number[], options?: {
    cv?: number | SplitterLike;
    groups?: unknown[];
}): number[];
export * from './modelSelectionExtra';