Report for a cross-validation of an estimator

The class CrossValidationReport performs cross-validation and provides a report to inspect and evaluate a scikit-learn estimator in an interactive way. The functionalities of the report are exposed through accessors.

CrossValidationReport(estimator, X[, y, ...])

Report for cross-validation results.

Methods

CrossValidationReport.help()	Display available methods using rich.
CrossValidationReport.cache_predictions([...])	Cache the predictions for sub-estimators reports.
CrossValidationReport.clear_cache()	Clear the cache.
CrossValidationReport.get_predictions(*, ...)	Get estimator's predictions.

Metrics

CrossValidationReport.metrics

Accessor for metrics-related operations.

Metrics

The metrics accessor helps you to evaluate the statistical performance of your estimator across cross-validation folds.

CrossValidationReport.metrics.help	Display available methods using rich.
CrossValidationReport.metrics.report_metrics	Report a set of metrics for our estimator.
CrossValidationReport.metrics.custom_metric	Compute a custom metric.
CrossValidationReport.metrics.accuracy	Compute the accuracy score.
CrossValidationReport.metrics.brier_score	Compute the Brier score.
CrossValidationReport.metrics.log_loss	Compute the log loss.
CrossValidationReport.metrics.precision	Compute the precision score.
CrossValidationReport.metrics.precision_recall	Plot the precision-recall curve.
CrossValidationReport.metrics.prediction_error	Plot the prediction error of a regression model.
CrossValidationReport.metrics.r2	Compute the R² score.
CrossValidationReport.metrics.recall	Compute the recall score.
CrossValidationReport.metrics.rmse	Compute the root mean squared error.
CrossValidationReport.metrics.roc	Plot the ROC curve.
CrossValidationReport.metrics.roc_auc	Compute the ROC AUC score.
CrossValidationReport.metrics.timings	Get all measured processing times related to the estimator.