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 \(C_{0,1}\).
Read more in the User Guide.
Parameters: 


Returns: 

[1]  Wikipedia entry for the Confusion matrix (Wikipedia and other references may use a different convention for axes) 
>>> 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]])
In the binary case, we can extract true positives, etc as follows:
>>> tn, fp, fn, tp = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel() >>> (tn, fp, fn, tp) (0, 2, 1, 1)
sklearn.metrics.confusion_matrix
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http://scikitlearn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html