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, colorbar=True)[source]¶
DEPRECATED: Function
plot_confusion_matrix
is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.Plot Confusion Matrix.
Read more in the User Guide.
Deprecated since version 1.0:
plot_confusion_matrix
is deprecated in 1.0 and will be removed in 1.2. Use one of the following class methods:from_predictions
orfrom_estimator
.- Parameters
- estimatorestimator instance
Fitted classifier or a fitted
Pipeline
in which the last estimator is a classifier.- X{array-like, sparse matrix} of shape (n_samples, n_features)
Input values.
- y_truearray-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 iny_true
ory_pred
are used in sorted order.- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- normalize{‘true’, ‘pred’, ‘all’}, default=None
Either to normalize the counts display in the matrix:
if
'true'
, the confusion matrix is normalized over the true conditions (e.g. rows);if
'pred'
, the confusion matrix is normalized over the predicted conditions (e.g. columns);if
'all'
, the confusion matrix is normalized by the total number of samples;if
None
(default), the 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 ofy_true
andy_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 ‘d’ or ‘.2g’ whichever is shorter.- 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.- colorbarbool, default=True
Whether or not to add a colorbar to the plot.
New in version 0.24.
- Returns
- display
ConfusionMatrixDisplay
- display
See also
confusion_matrix
Compute Confusion Matrix to evaluate the accuracy of a classification.
ConfusionMatrixDisplay
Confusion Matrix visualization.
Examples
>>> import matplotlib.pyplot as plt >>> from sklearn.datasets import make_classification >>> from sklearn.metrics import plot_confusion_matrix >>> from sklearn.model_selection import train_test_split >>> from sklearn.svm import SVC >>> X, y = make_classification(random_state=0) >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, random_state=0) >>> clf = SVC(random_state=0) >>> clf.fit(X_train, y_train) SVC(random_state=0) >>> plot_confusion_matrix(clf, X_test, y_test) >>> plt.show()