I have trained a model in keras and I have made some predictions. To evaluate the performance of my model I have calculated the precision and recall scores and the confusion matrix with sklearn library. This is my code:
final_predictions = model.predict_generator(generator_test, steps=steps_per_epoch_test)
rounded_pred = [0 if x<=0.5 else 1 for x in final_predictions]
test_precision_score = round(precision_score(y_test, rounded_pred), 3)
test_recall_score = round(recall_score(y_test, rounded_pred), 3)
test_confusion_matrix = confusion_matrix(y_test, rounded_pred)
These are my results:
Test confusion matrix :
[[1555 13]
[ 9 49]]
Precision and recall:
Test Precision :: 0.845
Test Recall :: 0.79
Does somebody know why is the precision score calculated incorrectly? (It should be (1555/(1555+13) instead of (13/(13+49)))
The default pos_label of precision_score and recall_score are 1.
from sklearn.metrics import confusion_matrix,precision_score,recall_score,classification_report
y_true = [0]*1568 + [1]*58
y_pred = [0]*1555 + [1]*13 + [0]* 9+ [1]* 49
print('confusion matrix :\n',confusion_matrix(y_true,y_pred))
print('precision_score :\n',precision_score(y_true,y_pred,pos_label=1))
print('recall_score :\n',recall_score(y_true,y_pred,pos_label=1))
print('classification_report :\n',classification_report(y_true,y_pred))
confusion matrix :
[[1555 13]
[ 9 49]]
precision_score :
0.7903225806451613
recall_score :
0.8448275862068966
classification_report :
precision recall f1-score support
0 0.99 0.99 0.99 1568
1 0.79 0.84 0.82 58
micro avg 0.99 0.99 0.99 1626
macro avg 0.89 0.92 0.90 1626
weighted avg 0.99 0.99 0.99 1626
If you want to get precision_score and recall_score of label=1. You can set pos_label=0 to set class.
print('precision_score :\n',precision_score(y_true,y_pred,pos_label=0))
print('recall_score :\n',recall_score(y_true,y_pred,pos_label=0))
precision_score :
0.9942455242966752
recall_score :
0.9917091836734694