Asked about Metrics in code, with the traps

When would you use macro F1 instead of accuracy?

An answer that works

When classes are imbalanced and each one matters. Accuracy is dominated by the common classes; macro F1 averages per-class F1 equally, so a model that ignores a rare class is penalised.

Then they push further

Your weighted F1 is 0.91 and macro F1 is 0.62. What does that tell you?

Common classes are handled well and at least one rare class badly. Look at per-class scores and the confusion matrix to find which, then consider more data for it, class weights, or whether the class definition is clear.

What gets marked up

Marked up for explaining the gap between averages and moving straight to per-class analysis.

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