sklearn.metrics.confusion_matrix()

sklearn.metrics.confusion_matrix(y_true, y_pred, labels=None, sample_weight=None) [source]

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 $j$ .

Thus in binary classification, the count of true negatives is $C_{0,0}$ , false negatives is $C_{1,0}$ , true positives is $C_{1,1}$ and false positives is $C_{0,1}$ .

Parameters:	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. sample_weight : array-like of shape = [n_samples], optional Sample weights.
Returns:	C : array, shape = [n_classes, n_classes] Confusion matrix

References

[R203]

Wikipedia entry for the Confusion matrix

Examples

>>> 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]])

>>> y_true = ["cat", "ant", "cat", "cat", "ant", "bird"]
>>> y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"]
>>> confusion_matrix(y_true, y_pred, labels=["ant", "bird", "cat"])
array([[2, 0, 0],
       [0, 0, 1],
       [1, 0, 2]])