Confusion matrix in sklearn

Question

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.

Source: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html

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.

Answer 1

You just confused the actual and predicted. Every row represents actual values of distinct elements in your array and columns represent predicted values of them. That is,

• First row: There are 2 ants, and 2 samples are predicted as ant.
• Seconds row: There are 1 bird and 1 sample is predicted as cat.
• Third row: There are 3 cats, 1 sample is predicted as ant, 2 samples are predicted as cat.