Scikit-learn confusion matrix

Question

I can\'t figure out if I\'ve setup my binary classification problem correctly. I labeled the positive class 1 and the negative 0. However It is my understanding that by default

Answer 1

Following the example of wikipedia. If a classification system has been trained to distinguish between cats and non cats, a confusion matrix will summarize the results of testing the algorithm for further inspection. Assuming a sample of 27 animals — 8 cats, and 19 non cats, the resulting confusion matrix could look like the table below:

With sklearn

If you want to maintain the structure of the wikipedia confusion matrix, first go the predicted values and then the actual class.

from sklearn.metrics import confusion_matrix
y_true = [0,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,1,1,0,1,0,0,0,0]
y_pred = [0,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0]
confusion_matrix(y_pred, y_true, labels=[1,0])

Out[1]: 
array([[ 5,  2],
       [ 3, 17]], dtype=int64)

Another way with crosstab pandas

true = pd.Categorical(list(np.where(np.array(y_true) == 1, 'cat','non-cat')), categories = ['cat','non-cat'])
pred = pd.Categorical(list(np.where(np.array(y_pred) == 1, 'cat','non-cat')), categories = ['cat','non-cat'])

pd.crosstab(pred, true, 
            rownames=['pred'], 
            colnames=['Actual'], margins=False, margins_name="Total")

Out[2]: 
Actual   cat  non-cat
pred                 
cat        5        2
non-cat    3       17

I hope it serves you