Deep-Learning-with-PyTorch

322 CHAPTER 12 Improving training with metrics and augmentation
A false negative is an event that is classified as not of interest or not a member of the
desired class (negative as in “No, that’s not the type of thing I’m interested in knowing
about”) but that in truth is actually of interest. For the nodule-detection problem, it’s
when a nodule (that is, a potential cancer) goes undetected. For Preston, these would
be the robberies that he sleeps through. We’ll get a bit creative here and use a picture
of a rodent burglar for false negatives. They’re sneaky!
Contrast false negatives with true negatives: uninteresting items that are correctly
identified as such. We’ll go with a picture of a bird for these.
Just to complete the metaphor, chapter 11’s model is basically a cat that refuses to
meow at anything that isn’t a can of tuna (while stoically ignoring Roxie). Our focus at
the end of the last chapter was on the percent correct for the overall training and validation
sets. Clearly, that wasn’t a great way to grade ourselves, and as we can see from
each of our dogs’ myopic focus on a single metric—like the number of true positives or
true negatives—we need a metric with a broader focus to capture our overall performance.
12.3 Graphing the positives and negatives
Let’s start developing the visual language we’ll use to describe true/false positives/
negatives. Please bear with us if our explanation gets repetitive; we want to make sure
you develop a solid mental model for the ratios we’re going to discuss. Consider figure
12.4, which shows events that might be of interest to one of our guard dogs.
True
Negative
Dog Prediction
Threshold
False
Positive
Human
ClaSsification
Threshold
False
Negative
True
Positive
Ignore
Bark
Figure 12.4 Cats, birds, rodents, and robbers make up our four classification
quadrants. They are separated by a human label and the dog classification threshold.