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.

estimatorestimator instance

Trained classifier.

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

Input values.

yarray-like of shape (n_samples,)

Target values.

labelsarray-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_weightarray-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_labelsarray-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_valuesbool, default=True

Includes values in confusion matrix.

xticks_rotation{‘vertical’, ‘horizontal’} or float, default=’horizontal’

Rotation of xtick labels.

values_formatstr, default=None

Format specification for values in confusion matrix. If None, the format specification is ‘.2g’.

cmapstr or matplotlib Colormap, default=’viridis’

Colormap recognized by matplotlib.

axmatplotlib Axes, default=None

Axes object to plot on. If None, a new figure and axes is created.


Examples using sklearn.metrics.plot_confusion_matrix