`sklearn.metrics`.plot_confusion_matrix¶

sklearn.metrics.plot_confusion_matrix(estimator, X, y_true, labels=None, sample_weight=None, normalize=None, display_labels=None, include_values=True, xticks_rotation='horizontal', values_format=None, cmap='viridis', ax=None)[source]¶

Plot Confusion Matrix.

Read more in the User Guide.

Parameters

estimator : estimator instance

Trained classifier.

X : {array-like, sparse matrix} of shape (n_samples, n_features)

Input values.

y : array-like of shape (n_samples,)

Target values.

labels : array-like of shape (n_classes,), default=None

List of labels to index the matrix. This may be used to reorder or select a subset of labels. If None is given, those that appear at least once in y_true or y_pred are used in sorted order.

sample_weight : array-like of shape (n_samples,), default=None

Sample weights.

normalize : {‘true’, ‘pred’, ‘all’}, default=None

Normalizes confusion matrix over the true (rows), predicted (columns) conditions or all the population. If None, confusion matrix will not be normalized.

display_labels : array-like of shape (n_classes,), default=None

Target names used for plotting. By default, labels will be used if it is defined, otherwise the unique labels of y_true and y_pred will be used.

include_values : bool, default=True