Macro VS Micro VS Weighted VS Samples F1 Score

Question

In sklearn.metrics.f1_score, the f1 score has a parameter called "average". What does macro, micro, weighted, and samples mean? Please elaborate, because in the documentation, it was not explained properly. Or simply answer the following:

1. Why is "samples" best parameter for multilabel classification?
2. Why is micro best for an imbalanced dataset?
3. what's the difference between weighted and macro?

Answer 1

The question is about the meaning of the average parameter in sklearn.metrics.f1_score.

As you can see from the code:

• average=micro says the function to compute f1 by considering total true positives, false negatives and false positives (no matter of the prediction for each label in the dataset)
• average=macro says the function to compute f1 for each label, and returns the average without considering the proportion for each label in the dataset.
• average=weighted says the function to compute f1 for each label, and returns the average considering the proportion for each label in the dataset.
• average=samples says the function to compute f1 for each instance, and returns the average. Use it for multilabel classification.