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 iny_true
ory_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 ofy_true
andy_pred
will be used.include_values : bool, default=True
Includes values in confusion matrix.
xticks_rotation : {‘vertical’, ‘horizontal’} or float, default=’horizontal’
Rotation of xtick labels.
values_format : str, default=None
Format specification for values in confusion matrix. If
None
, the format specification is ‘.2g’.cmap : str or matplotlib Colormap, default=’viridis’
Colormap recognized by matplotlib.
ax : matplotlib Axes, default=None
Axes object to plot on. If
None
, a new figure and axes is created.- Returns
display :
ConfusionMatrixDisplay