Neural Networks with R

Precision and recall

Precision and recall are again ratios between the TP with (TP+FP) and TP with (TP+FN) respectively. These ratios determine how relevant our predictions are compared to the actual.

Precision is defined as how many selected items are relevant. That is, how many of the predicted ones are actually correctly predicted.

The equation is:

 

If precision is closer to one, we are more accurate in our predictions.

Recall, on the other hand, tells how many relevant items we selected. Mathematically, it is:

 

 

The following diagram ...