API

This page lists all the public functions and classes of the skore package.

Warning

This code is still in development. The API is subject to change.

Overview

The following table provides a quick reference to the public classes and functions in skore:

Project Management

Project

Main class for managing a skore project and its reports

ML Assistance

train_test_split()

Split arrays or matrices into random train and test subsets

EstimatorReport	Report for a fitted estimator Accessor methods Data: EstimatorReport.data analyze() Plot dataset statistics help() Display accessor help using rich or HTML Metrics: EstimatorReport.metrics accuracy() Compute the accuracy score brier_score() Compute the Brier score confusion_matrix() Plot the confusion matrix custom_metric() Compute a custom metric help() Display accessor help using rich or HTML log_loss() Compute the log loss precision() Compute the precision score precision_recall() Plot the precision-recall curve prediction_error() Plot the prediction error of a regression model r2() Compute the R² score recall() Compute the recall score rmse() Compute the root mean squared error roc() Plot the ROC curve roc_auc() Compute the ROC AUC score summarize() Report a set of metrics for our estimator timings() Get all measured processing times related to the estimator Inspection: EstimatorReport.inspection coefficients() Retrieve the coefficients of a linear model, including the intercept help() Display accessor help using rich or HTML impurity_decrease() Retrieve the Mean Decrease in Impurity (MDI) of a tree-based model permutation_importance() Display the permutation feature importance
CrossValidationReport	Report for cross-validation results Accessor methods Data: CrossValidationReport.data analyze() Plot dataset statistics help() Display accessor help using rich or HTML Metrics: CrossValidationReport.metrics accuracy() Compute the accuracy score brier_score() Compute the Brier score confusion_matrix() Plot the confusion matrix custom_metric() Compute a custom metric help() Display accessor help using rich or HTML log_loss() Compute the log loss precision() Compute the precision score precision_recall() Plot the precision-recall curve prediction_error() Plot the prediction error of a regression model r2() Compute the R² score recall() Compute the recall score rmse() Compute the root mean squared error roc() Plot the ROC curve roc_auc() Compute the ROC AUC score summarize() Report a set of metrics for our estimator timings() Get all measured processing times related to the estimator Inspection: CrossValidationReport.inspection coefficients() Retrieve the coefficients across splits, including the intercept help() Display accessor help using rich or HTML impurity_decrease() Retrieve the Mean Decrease in Impurity (MDI) for each split permutation_importance() Display the permutation feature importance
ComparisonReport	Report for comparing reports Accessor methods Metrics: ComparisonReport.metrics accuracy() Compute the accuracy score brier_score() Compute the Brier score confusion_matrix() Plot the confusion matrix custom_metric() Compute a custom metric help() Display accessor help using rich or HTML log_loss() Compute the log loss precision() Compute the precision score precision_recall() Plot the precision-recall curve prediction_error() Plot the prediction error of a regression model r2() Compute the R² score recall() Compute the recall score rmse() Compute the root mean squared error roc() Plot the ROC curve roc_auc() Compute the ROC AUC score summarize() Report a set of metrics for the estimators timings() Get all measured processing times related to the different estimators Inspection: ComparisonReport.inspection coefficients() Retrieve the coefficients for each report, including the intercepts help() Display accessor help using rich or HTML impurity_decrease() Retrieve the Mean Decrease in Impurity (MDI) for each report permutation_importance() Display the permutation feature importance

Display Classes

Data

TableReportDisplay

Display for tabular data reports

Metrics

MetricsSummaryDisplay	Display for summarizing multiple metrics
ConfusionMatrixDisplay	Confusion matrix visualization
RocCurveDisplay	ROC (Receiver Operating Characteristic) curve visualization
PrecisionRecallCurveDisplay	Precision-Recall curve visualization
PredictionErrorDisplay	Prediction error visualization

Inspection

CoefficientsDisplay	Display for visualizing feature importance via model coefficients
ImpurityDecreaseDisplay	Display for visualizing feature importance via Mean Decrease in Impurity (MDI)
PermutationImportanceDisplay	Display for visualizing feature importance via permutation importance

Utilities

configuration	Global configuration for skore also usable as a context manager
show_versions()	Print version information for skore and its dependencies