Hướng dẫn dùng confusionmatrixdisplay python

Confusion Matrix visualization.

It is recommend to use from_estimator or from_predictions to create a ConfusionMatrixDisplay. All parameters are stored as attributes.

Read more in the User Guide.

Confusion matrix.

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

Image representing the confusion matrix.

Array of matplotlib axes. None if include_values is false.

Axes with confusion matrix.

Figure containing the confusion matrix.

Examples

>>> import matplotlib.pyplot as plt
>>> 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[]

>>> plt.show[]

Methods

Plot Confusion Matrix given an estimator and some data.

Read more in the User Guide.

New in version 1.0.

Fitted classifier or a fitted Pipeline in which the last estimator is a classifier.

Input values.

Target values.

List of labels to index the confusion 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 weights.

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.

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.

Includes values in confusion matrix.

Rotation of xtick labels.

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

Colormap recognized by matplotlib.

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

Whether or not to add a colorbar to the plot.

Dict with keywords passed to matplotlib.pyplot.imshow call.

Examples

>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import make_classification
>>> from sklearn.metrics import 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]
>>> ConfusionMatrixDisplay.from_estimator[
...     clf, X_test, y_test]

>>> plt.show[]

Plot Confusion Matrix given true and predicted labels.

Read more in the User Guide.

New in version 1.0.

True labels.

The predicted labels given by the method predict of an classifier.

List of labels to index the confusion 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 weights.

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.

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.

Includes values in confusion matrix.

Rotation of xtick labels.

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

Colormap recognized by matplotlib.

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

Whether or not to add a colorbar to the plot.

Dict with keywords passed to matplotlib.pyplot.imshow call.

Examples

>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import make_classification
>>> from sklearn.metrics import 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]
>>> y_pred = clf.predict[X_test]
>>> ConfusionMatrixDisplay.from_predictions[
...    y_test, y_pred]

>>> plt.show[]

Plot visualization.

Includes values in confusion matrix.

Colormap recognized by matplotlib.

Rotation of xtick labels.

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

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

Whether or not to add a colorbar to the plot.

Dict with keywords passed to matplotlib.pyplot.imshow call.