Classification report question I need some help to interpret the 2 classification reports of the same logistic regression. The only difference between them is the size of test_size. Even though my second classification report has a much higher f1-score, because of its low test_size (0.1), it is an inferior algorithm than my first classification report?
I am not wholly sure how to interpret the relationship between the f1-score and support values. But from what I understand, the support values should all be close to each other and f1-score be as close to 1 as possible. In that case does that make the algorithm I developed a bad one then?
Support is the number of occurrences of each label in the ground truth. For example in the results with test size=0.1, class P has only 3 samples. Based on this, if the support values are not close to each other, it only means that your data is unbalanced. This documentation might be helpful.