# Select two best classifier using F1-score,Recall and precision

I have three classifiers that classify same dataset with these results:

classifier A:
precision    recall   f1-score
micro avg       0.36      0.36      0.36
macro avg       0.38      0.43      0.36
weighted avg    0.36      0.36      0.32

classifier B:
precision    recall   f1-score
micro avg       0.55      0.55      0.55
macro avg       0.60      0.60      0.56
weighted avg    0.61      0.55      0.53

classifier C:
precision    recall   f1-score
micro avg       0.34      0.34      0.34
macro avg       0.36      0.38      0.32
weighted avg    0.39      0.34      0.32


I want two select two best of them, and I know F1-score is a parameter for compare the classifiers because of its harmony between precision and recall. So, at first I select classifier B for its best F1-score. for next, both A and C have a same F1-measure, I want to ask how can I select between them?

## 2 Answers

f1-score combines precision and recall in a single figure. As both are pretty similar in A and C cases, f1-score is similar too.

Your choice depends on what it is less harmful in your categorization: false positives or false negatives.

I do recommend you to read the 3rd chapter of "DEEP LEARNING:From Basics to Practice" volume 1 by Andrew Glassner. There you have the three concepts (precision, recall and f1-score) described in a very illustrative way.

• they are not comparable if we dont have FP or FN? – Saha Dec 28 '18 at 12:07
• You do not need direct access to FN or FN values. Precision and recall only varies in a part of their fraction formulas which includes FN or FP, so your displayed values reflect their relationship. What Nga Dao and I mean is that your choice depends on the impact of false positives or false negatives in your system. Nga Dao example is quite nice. – David Dec 29 '18 at 8:16

It depends on your application. Assume that you design a classifier model to predict whether a person has cancer. If you wanna say confidently that a person has cancer, you probably prefer a classifier with high precision. On the other hand, if you want to make sure all people with cancer will be caught, you probably prefer a classifier with high recall.