- 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 is such that is equal to the number of observations known to be in group but predicted to be in group .
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]
List of all labels occuring in the dataset. If none is given, those that appear at least once in y_true or y_pred are used.
C : array, shape = [n_classes, n_classes]
>>> 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]])