# Confusion matrix in sklearn

If you look at this:

>>> 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]])


I suppose fist row of array means "predicted ant" and first column is "actually is ant" second column is "actually is bird" etc.

So first row first col 2 i read like "predicted ant, is ant", first row second col 0 i read as "precited ant is bird" is 0 which fits, and third column is "predicted ant is cat" is 0 but should be 1.

What i am doing wrong while understanding the confusion matrix.

Another example is this

>>> 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]])


Where is not even clear, what is the order of classes.

edit: Unless it is swapped. First row is "is ant" not "predicted ant". Only that on wikipedia the system is that row is the prediction.

• Did you see this line in the docs you linked to? "Returns: ... Confusion matrix whose i-th row and j-th column entry indicates the number of samples with true label being i-th class and prediced label being j-th class". And also "Wikipedia and other references may use a different convention for axes". Nov 2 '20 at 1:57