Accuracy (F1)
The Accuracy (or the F1 score) is a measure that combines recall and precision. There is a trade-off between precision and recall, F1 can therefore be used to measure how effectively our models make that trade-off.
One important feature of the F1 score is that the result is zero if any of the components (precision or recall) fall to zero. Thereby it penalises extreme negative values of either component.
Precision
Precision is a metric that gives you the proportion of true positives to the amount of total positives that the model predicts. It answers the question:
“Out of all the positive predictions we made, how many were true?”
Recall
Recall focuses on how good the model is at finding all the positives. Recall is also called true positive rate and answers the question:
“Out of all the data points that should be predicted as true, how many did we correctly predict as true?”
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