I wanted to compare the manual computations of the precision and recall with scikit-learn functions. However, recall_score()
and precision_score()
of scikit-learn functions gave me different results. Not sure why! Could you please give me some advice why I am getting different results? Thanks!
My confusion matrix:
tp, fn, fp, tn = confusion_matrix(y_test, y_test_pred).ravel() print('Outcome values : n', tp, fn, fp, tn)
Outcome values : 3636933 34156 127 151
FDR=tp/(tp+fn) # TPR/Recall/Sensitivity print('Recall: %.3f' % FDR)
Recall: 0.991
precision=tp/(tp + fp) print('Precision: %.3f' % precision)
Precision: 1.000
precision = precision_score(y_test, y_test_pred) print('Precision: %f' % precision) recall = recall_score(y_test, y_test_pred) print('Recall: %f' % recall)
Precision: 0.004401 Recall: 0.543165
Advertisement
Answer
It should be (check return value’s ordering):
tn, fp, fn, tp = confusion_matrix(y_test, y_test_pred).ravel()
Please refer: here