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sklearn.metrics.confusion_matrix(y_true, y_pred, labels=None)

Compute confusion matrix to evaluate the accuracy of a classification

By definition a confusion matrix C is such that C_{i, j} is equal to the number of observations known to be in group i but predicted to be in group j.


y_true : array, shape = [n_samples]

Ground truth (correct) target values.

y_pred : array, shape = [n_samples]

Estimated targets as returned by a classifier.

labels : array, shape = [n_classes], optional

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 in y_true or y_pred are used in sorted order.


C : array, shape = [n_classes, n_classes]

Confusion matrix


[R50]Wikipedia entry for the Confusion matrix


>>> from sklearn.metrics import confusion_matrix
>>> y_true = [2, 0, 2, 2, 0, 1]
>>> y_pred = [0, 0, 2, 2, 0, 2]
>>> confusion_matrix(y_true, y_pred)
array([[2, 0, 0],
       [0, 0, 1],
       [1, 0, 2]])