Macro VS Micro VS Weighted VS Samples F1 Score

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.