Module cv 

Cross-validation utilities and unified CV framework.

This module provides:

• Shared fold assignment utilities used across all CV functions
• cv_fdata: Generic k-fold + repeated CV framework (R’s cv.fdata)

Structs

CvFdataResult

Result of unified cross-validation.

CvSelectionResult

Generic cross-validation result for hyperparameter selection.

Enums

CvMetrics

Cross-validation metrics.

CvType

Type of cross-validation task.

Functions

classification_metrics

Default classification metric set: accuracy, precision, recall, F1.

create_folds

Assign observations to folds (deterministic given seed).

create_stratified_folds

Assign observations to stratified folds (classification).

cv_fdata

Generic k-fold + repeated cross-validation framework (R’s cv.fdata).

cv_fdata_with_metrics

Generic k-fold + repeated CV with user-defined metrics.

fold_indices

Split indices into train and test sets for a given fold.

metric_accuracy

Classification accuracy.

metric_f1

F1 score (harmonic mean of precision and recall).

metric_mae

Mean Absolute Error.

metric_precision

Macro (binary) precision: TP / (TP + FP).

metric_r_squared

Coefficient of determination (R-squared).

metric_recall

Macro (binary) recall: TP / (TP + FN).

metric_rmse

Root Mean Squared Error.

regression_metrics

Default regression metric set: RMSE, MAE, R-squared.

subset_rows

Extract a sub-matrix from an FdMatrix by selecting specific row indices.

subset_vec

Extract elements from a slice by indices.

Type Aliases

MetricFn

A named metric function: (name, fn(y_true, y_pred) -> f64).