Simplifying Precision, Recall and F1 Score

Explaining evaluation metrics in basic terms

Machine learning terms can seem very convoluted, as if they were made to be understood by machines. Unintuitive and similar sounding names like False Negatives and True Positives, Precision, Recall, Area Under ROC, Sensitivity, Specificity and Insanity. Ok, the last one wasn’t real.

There are some great articles on precision and recall already, but when I read them and other discussions on stackexchange, the messy terms all mix up in my mind and I’m left more…