import { XGBTree } from './xgbTree';
export interface XGBoostProps {
    nEstimators?: number;
    n_estimators?: number;
    learningRate?: number;
    learning_rate?: number;
    maxDepth?: number;
    max_depth?: number;
    lambda?: number;
    reg_lambda?: number;
    gamma?: number;
    minChildWeight?: number;
    min_child_weight?: number;
    subsample?: number;
    colsampleByTree?: number;
    colsample_bytree?: number;
    baseScore?: number;
    base_score?: number;
    randomState?: number;
    random_state?: number;
}
/**
 * Resolved, validated hyper-parameters shared by the XGBoost models
 * (canonical camelCase keys — exactly the shape returned by getParams()).
 */
export interface XGBoostParams {
    nEstimators: number;
    learningRate: number;
    maxDepth: number;
    lambda: number;
    gamma: number;
    minChildWeight: number;
    subsample: number;
    colsampleByTree: number;
    baseScore: number;
    randomState: number | undefined;
}
/**
 * Resolve props (accepting both camelCase and snake_case aliases) to the
 * canonical parameter set, following xgboost's defaults: eta=0.3,
 * max_depth=6, lambda=1, gamma=0, min_child_weight=1, subsample=1,
 * colsample_bytree=1, base_score=0.5.
 */
export declare function resolveXGBoostProps(props?: XGBoostProps): XGBoostParams;
/**
 * Objective: first/second-order gradients of the loss at margin F.
 * yInternal is whatever fitBoostedTrees() was given (raw targets for
 * regression, 0/1 labels for classification).
 */
export type GradientFn = (F: number[], yInternal: number[]) => {
    g: number[];
    h: number[];
};
/**
 * Shared boosting loop for the XGBoost models (exact greedy). Returns the
 * fitted trees; the caller stores them together with the initial margin.
 */
export declare function fitBoostedTrees(X: number[][], yInternal: number[], initialMargin: number, params: XGBoostParams, gradients: GradientFn): XGBTree[];
export declare function predictBoostedMargin(trees: XGBTree[], baseMargin: number, learningRate: number, testX: number[][]): number[];
export declare function validateXGBoostFitInput(X: number[][], y: number[]): void;