import { BaseEstimator, TransformerBase } from '../base';
import { Params } from '../base/estimator';
export type SplineKnots = 'uniform' | 'quantile' | number[][];
export type SplineExtrapolation = 'error' | 'constant' | 'linear' | 'continue' | 'periodic';
export interface SplineTransformerProps {
    nKnots?: number;
    degree?: number;
    knots?: SplineKnots;
    extrapolation?: SplineExtrapolation;
    includeBias?: boolean;
}
/** Univariate B-spline basis expansion applied independently to every numeric feature. */
export declare class SplineTransformer extends TransformerBase {
    private nKnots;
    private degree;
    private knots;
    private extrapolation;
    private includeBias;
    private knotVectorsState;
    private boundariesState;
    private nFeaturesState;
    constructor(props?: SplineTransformerProps);
    getParams(): Params;
    fit(X: number[][]): void;
    private basisAtDegree;
    private basis;
    private basisDerivative;
    private featureBasis;
    transform(X: number[][]): number[][];
}
export type Category = string | number | boolean | null;
export type TargetLabel = string | number | boolean;
export interface TargetEncoderProps {
    categories?: 'auto' | Category[][];
    targetType?: 'auto' | 'continuous' | 'binary' | 'multiclass';
    smooth?: 'auto' | number;
    cv?: number;
    shuffle?: boolean;
    randomState?: number;
}
/** Supervised categorical encoder with smoothed target means and leakage-safe cross-fitted fitTransform. */
export declare class TargetEncoder extends BaseEstimator {
    private categories;
    private targetType;
    private smooth;
    private cv;
    private shuffle;
    private randomState?;
    private statsState?;
    constructor(props?: TargetEncoderProps);
    getParams(): Params;
    private validate;
    private prepareTargets;
    private compute;
    fit(X: Category[][], y: TargetLabel[]): void;
    private encode;
    transform(X: Category[][]): number[][];
    fitTransform(X: Category[][], y: TargetLabel[]): number[][];
}