As we saw previously, we achieved a test accuracy of 88% at the last epoch of our training session. Let's have a look at what this really means, by interpreting the precision and recall scores of our classifier:
As we noticed previously, the ratio of correctly predicted positive observations to the total number of positive observations in our test set (otherwise known as the precision score) is pretty high at 0.98. The recall score is a bit lower and denotes the number of correctly predicted results divided by the number of results that should have been returned. Finally, the F-measure simply combines both the precision and recall scores as a harmonic mean.
To supplement our understanding, we plot out a confusion matrix of our classifier on the test set, as shown as follows. This is essentially an error matrix that lets us visualize how our model...