Deep-Learning-with-PyTorch

330 CHAPTER 12 Improving training with metrics and augmentation
recaLl
avg(p, r)
f1(p, r)
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precision
precision
Figure 12.11 Computing the final score with avg(p, r). Lighter values are closer to 1.0.
recaLl
Contrast that with the F1 score: when recall is high but precision is low, trading off a
lot of recall for even a little precision will move the score closer to that balanced sweet
spot. There’s a nice, deep elbow that is easy to slide into. That encouragement to have
balanced precision and recall is what we want from our grading metric.
Let’s say we still want a simpler metric, but one that doesn’t reward skew at all. In
order to correct for the weakness of addition, we might take the minimum of precision
and recall (figure 12.12).
min(p, r)
f1(p, r)
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precision
precision
Figure 12.12 Computing the final score with min(p, r)