@astermind/astermind-elm/dist/core/Evaluation.d.ts

JavaScript Extreme Learning Machine (ELM) library for browser and Node.js.

export interface PerClassMetrics {
    label: string | number;
    support: number;
    tp: number;
    fp: number;
    fn: number;
    tn: number;
    precision: number;
    recall: number;
    f1: number;
}
export interface ClassificationAverages {
    accuracy: number;
    macroPrecision: number;
    macroRecall: number;
    macroF1: number;
    microPrecision: number;
    microRecall: number;
    microF1: number;
    weightedPrecision: number;
    weightedRecall: number;
    weightedF1: number;
    logLoss?: number;
    topKAccuracy?: number;
}
export interface ClassificationReport {
    confusionMatrix: number[][];
    perClass: PerClassMetrics[];
    averages: ClassificationAverages;
}
export interface RegressionPerOutput {
    index: number;
    mse: number;
    rmse: number;
    mae: number;
    r2: number;
}
export interface RegressionReport {
    perOutput: RegressionPerOutput[];
    mse: number;
    rmse: number;
    mae: number;
    r2: number;
}
export declare function confusionMatrixFromIndices(yTrueIdx: number[], yPredIdx: number[], C?: number): number[][];
export declare function logLoss(yTrue: number[] | number[][], yPredProba: number[] | number[][]): number;
export declare function topKAccuracy(yTrueIdx: number[], yPredProba: number[][], k?: number): number;
export interface BinaryCurve {
    thresholds: number[];
    tpr?: number[];
    fpr?: number[];
    precision?: number[];
    recall?: number[];
    auc: number;
}
export declare function binaryROC(yTrue01: number[], yScore: number[]): BinaryCurve;
export declare function binaryPR(yTrue01: number[], yScore: number[]): BinaryCurve;
/**
 * Evaluate multi-class classification.
 * - yTrue can be indices (N) or one-hot (N x C)
 * - yPred can be indices (N) or probabilities (N x C)
 * - If yPred are probabilities, we also compute logLoss and optional topK.
 */
export declare function evaluateClassification(yTrue: number[] | number[][], yPred: number[] | number[][], opts?: {
    labels?: Array<string | number>;
    topK?: number;
}): ClassificationReport;
export declare function evaluateRegression(yTrue: number[] | number[][], yPred: number[] | number[][]): RegressionReport;
export declare function formatClassificationReport(rep: ClassificationReport): string;