python confusion matrix with labels code example

Example 1: confusion matrix python

By definition, entry i,j in a confusion matrix is the number of 
observations actually in group i, but predicted to be in group j. 
Scikit-Learn provides a confusion_matrix function:

from sklearn.metrics import confusion_matrix
y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2]
y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2]
confusion_matrix(y_actu, y_pred)
# Output
# array([[3, 0, 0],
#        [0, 1, 2],
#        [2, 1, 3]], dtype=int64)

Example 2: print labels on confusion_matrix

unique_label = np.unique([y_true, y_pred])
cmtx = pd.DataFrame(
    confusion_matrix(y_true, y_pred, labels=unique_label), 
    index=['true:{:}'.format(x) for x in unique_label], 
    columns=['pred:{:}'.format(x) for x in unique_label]
)
print(cmtx)
# Output:
#           pred:no  pred:yes
# true:no         3         0
# true:yes        2         1

Example 3: confusion matrix with labels sklearn

Predicted  0  1  2  All
True                   
0          3  0  0    3
1          0  1  2    3
2          2  1  3    6
All        5  2  5   12