Fast hyperparameter tuning

HUGIMLClassifier.tune provides a GridSearchCV-like interface. Eligible adaptive-binning grids can reuse cached mining work for faster validation. Unsupported grids automatically use ordinary per-candidate evaluation.

result = HUGIMLClassifier.tune(
    X_train,
    y_train,
    cv=5,
    scoring="roc_auc",
    param_grid={
        "adaptive_binning": [True],
        "G": [1e-2, 5e-3, 1e-3],
        "L": [1, 2],
        "topK": [30, 50, 100],
        "feature_mode": ["patterns_only", "original_plus_patterns"],
    },
    refit=True,
    use_fast_path=True,
)

print(result.best_params_)
print(result.best_score_)
print(result.results_)

Fast path eligibility

The cached path is intended for adaptive-binning grids where the varying dimensions are limited to mining and representation parameters such as G, L, topK, and feature_mode. Other grids remain valid and are evaluated through the standard path.