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.

Accessors

CrossValidationReport.data	Base class for all accessors.
CrossValidationReport.metrics	Accessor for metrics-related operations.

Data

The data accessor helps you to get insights about the dataset used in the cross-validation.

CrossValidationReport.data.help()	Display available methods using rich.
CrossValidationReport.data.analyze([with_y, ...])	Plot dataset statistics.

Metrics

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

CrossValidationReport.metrics.help()	Display available methods using rich.
CrossValidationReport.metrics.summarize(*[, ...])	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.