scitex_ml.classification.CrossValidationExperiment
- class scitex_ml.classification.CrossValidationExperiment(name, model_fn, cv=None, output_dir=None, metrics=None, save_models=True, verbose=True)[source]
Streamlined cross-validation experiment runner.
This class handles: - Cross-validation splitting - Model training and evaluation - Automatic metric calculation - Hyperparameter tracking - Progress monitoring - Report generation
- Parameters:
name (str) – Experiment name
model_fn (Callable) – Function that returns a model instance
cv (BaseCrossValidator, optional) – Cross-validation splitter (default: 5-fold stratified)
output_dir (Union[str, Path], optional) – Output directory for results
metrics (List[str], optional) – List of metrics to calculate
save_models (bool) – Whether to save trained models
verbose (bool) – Whether to print progress
- __init__(name, model_fn, cv=None, output_dir=None, metrics=None, save_models=True, verbose=True)[source]
Methods
__init__(name, model_fn[, cv, output_dir, ...])describe_dataset(X, y[, feature_names, ...])Record dataset information.
get_summary()Get summary statistics across folds.
get_validation_report()Get validation report.
run(X, y[, feature_names, class_names, ...])Run complete cross-validation experiment.
set_hyperparameters(**kwargs)Set hyperparameters for tracking.
- __init__(name, model_fn, cv=None, output_dir=None, metrics=None, save_models=True, verbose=True)[source]
- set_hyperparameters(**kwargs)[source]
Set hyperparameters for tracking.
- Parameters:
**kwargs – Hyperparameter key-value pairs
- Return type: