Pure Language Processing (NLP) fashions are in every single place — from chatbots and translation instruments to spam filters and sentiment evaluation. However how do we all know if these fashions are literally doing an excellent job? That’s the place analysis metrics are available in.
Whereas accuracy (how typically the mannequin is right) looks like the plain selection, it doesn’t all the time inform the total story — particularly when coping with imbalanced datasets (the place one class is far more widespread than the opposite). That is the place precision, recall, and F1-score step in. These metrics give us a clearer image of how nicely a mannequin is performing past simply “proper or unsuitable.”
On this article, we’ll break down these ideas in plain language, clarify why they matter, and present how they assist us make higher choices when working with NLP fashions.
