sklearn.metrics.ConfusionMatrixDisplay¶

class sklearn.metrics.ConfusionMatrixDisplay(confusion_matrix, *, display_labels=None)[source]¶

Confusion Matrix visualization.

It is recommend to use plot_confusion_matrix to create a ConfusionMatrixDisplay. All parameters are stored as attributes.

Read more in the User Guide.

Parameters

confusion_matrixndarray of shape (n_classes, n_classes)

Confusion matrix.

display_labelsndarray of shape (n_classes,), default=None

Display labels for plot. If None, display labels are set from 0 to n_classes - 1.

Attributes

im_matplotlib AxesImage

Image representing the confusion matrix.

text_ndarray of shape (n_classes, n_classes), dtype=matplotlib Text, or None

Array of matplotlib axes. None if include_values is false.

ax_matplotlib Axes

Axes with confusion matrix.

figure_matplotlib Figure

Figure containing the confusion matrix.

See also

confusion_matrix

Compute Confusion Matrix to evaluate the accuracy of a classification.

ConfusionMatrixDisplay.from_estimator

Plot the confusion matrix given an estimator, the data, and the label.

ConfusionMatrixDisplay.from_predictions

Plot the confusion matrix given the true and predicted labels.

Examples

>>> from sklearn.datasets import make_classification
>>> from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
>>> 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)
>>> predictions = clf.predict(X_test)
>>> cm = confusion_matrix(y_test, predictions, labels=clf.classes_)
>>> disp = ConfusionMatrixDisplay(confusion_matrix=cm,
...                               display_labels=clf.classes_)
>>> disp.plot() 

Methods

plot(*[, include_values, cmap, …])

Plot visualization.

plot(*, include_values=True, cmap='viridis', xticks_rotation='horizontal', values_format=None, ax=None, colorbar=True)[source]¶

Plot visualization.

Parameters

include_valuesbool, default=True

Includes values in confusion matrix.

cmapstr or matplotlib Colormap, default=’viridis’

Colormap recognized by matplotlib.

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.

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.

Returns

displayConfusionMatrixDisplay

Examples using sklearn.metrics.ConfusionMatrixDisplay¶