multilabel_confusion_matrix#

sklearn.metrics.multilabel_confusion_matrix(y_true,y_pred,*,sample_weight=None,labels=None,samplewise=False)[source]#

Compute a confusion matrix for each class or sample.

Added in version 0.21.

Compute class-wise (default) or sample-wise (samplewise=True) multilabelconfusion matrix to evaluate the accuracy of a classification, and outputconfusion matrices for each class or sample.

In multilabel confusion matrix\(MCM\), the count of true negativesis\(MCM_{:,0,0}\), false negatives is\(MCM_{:,1,0}\),true positives is\(MCM_{:,1,1}\) and false positives is\(MCM_{:,0,1}\).

Multiclass data will be treated as if binarized under a one-vs-resttransformation. Returned confusion matrices will be in the order ofsorted unique labels in the union of (y_true, y_pred).

Read more in theUser Guide.

Parameters:

y_true{array-like, sparse matrix} of shape (n_samples, n_outputs) or (n_samples,)

Ground truth (correct) target values.

y_pred{array-like, sparse matrix} of shape (n_samples, n_outputs) or (n_samples,)

Estimated targets as returned by a classifier.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

labelsarray-like of shape (n_classes,), default=None

A list of classes or column indices to select some (or to forceinclusion of classes absent from the data).

samplewisebool, default=False

In the multilabel case, this calculates a confusion matrix per sample.

Returns:

multi_confusionndarray of shape (n_outputs, 2, 2)

A 2x2 confusion matrix corresponding to each output in the input.When calculating class-wise multi_confusion (default), thenn_outputs = n_labels; when calculating sample-wise multi_confusion(samplewise=True), n_outputs = n_samples. Iflabels is defined,the results will be returned in the order specified inlabels,otherwise the results will be returned in sorted order by default.

See also

confusion_matrix

Compute confusion matrix to evaluate the accuracy of a classifier.

Notes

Themultilabel_confusion_matrix calculates class-wise or sample-wisemultilabel confusion matrices, and in multiclass tasks, labels arebinarized under a one-vs-rest way; whileconfusion_matrix calculates one confusion matrixfor confusion between every two classes.

Examples

Multilabel-indicator case:

>>>importnumpyasnp>>>fromsklearn.metricsimportmultilabel_confusion_matrix>>>y_true=np.array([[1,0,1],...[0,1,0]])>>>y_pred=np.array([[1,0,0],...[0,1,1]])>>>multilabel_confusion_matrix(y_true,y_pred)array([[[1, 0],        [0, 1]],       [[1, 0],        [0, 1]],       [[0, 1],        [1, 0]]])

Multiclass case:

>>>y_true=["cat","ant","cat","cat","ant","bird"]>>>y_pred=["ant","ant","cat","cat","ant","cat"]>>>multilabel_confusion_matrix(y_true,y_pred,...labels=["ant","bird","cat"])array([[[3, 1],        [0, 2]],       [[5, 0],        [1, 0]],       [[2, 1],        [1, 2]]])