[FIXED] sklearn: multi-class problem and reporting sensitivity and specificity

Issue

I have a three-class problem and I'm able to report precision and recall for each class with the below code:

from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred))

which gives me the precision and recall nicely for each of the 3 classes in a table format.

My question is how can I now get sensitivity and specificity for each of the 3 classes? I looked at sklearn.metrics and I didn't find anything for reporting sensitivity and specificity.

Solution

If we check the help page for classification report:

Note that in binary classification, recall of the positive class is also known as “sensitivity”; recall of the negative class is “specificity”.

So we can convert the pred into a binary for every class, and then use the recall results from precision_recall_fscore_support.

Using an example:

from sklearn.metrics import classification_report
y_true = [0, 1, 2, 2, 2]
y_pred = [0, 0, 2, 2, 1]
target_names = ['class 0', 'class 1', 'class 2']
print(classification_report(y_true, y_pred, target_names=target_names))

Looks like:

              precision    recall  f1-score   support

     class 0       0.50      1.00      0.67         1
     class 1       0.00      0.00      0.00         1
     class 2       1.00      0.67      0.80         3

    accuracy                           0.60         5
   macro avg       0.50      0.56      0.49         5
weighted avg       0.70      0.60      0.61         5

Using sklearn:

from sklearn.metrics import precision_recall_fscore_support
res = []
for l in [0,1,2]:
    prec,recall,_,_ = precision_recall_fscore_support(np.array(y_true)==l,
                                                      np.array(y_pred)==l,
                                                      pos_label=True,average=None)
    res.append([l,recall[0],recall[1]])

put the results into a dataframe:

pd.DataFrame(res,columns = ['class','sensitivity','specificity'])

    class   sensitivity specificity
0   0   0.75    1.000000
1   1   0.75    0.000000
2   2   1.00    0.666667

Answered By - StupidWolf