confusion_matrix#

CrossValidationReport.metrics.confusion_matrix(*, data_source='test')[source]#

Plot the confusion matrix.

The confusion matrix shows the counts of correct and incorrect classifications for each class.

Parameters:
data_source{“test”, “train”}, default=”test”

The data source to use.

  • “test” : use the test set provided when creating the report.

  • “train” : use the train set provided when creating the report.

Returns:
ConfusionMatrixDisplay

The confusion matrix display.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import CrossValidationReport
>>> X, y = load_breast_cancer(return_X_y=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = CrossValidationReport(classifier, X=X, y=y, splitter=2)
>>> display = report.metrics.confusion_matrix()
>>> display.plot()

With specific threshold for binary classification:

>>> display = report.metrics.confusion_matrix()
>>> display.plot(threshold_value=0.7)