On this third a part of my sequence, I’ll discover the analysis course of which is a crucial piece that can result in a cleaner knowledge set and elevate your mannequin efficiency. We are going to see the distinction between analysis of a educated mannequin (one not but in manufacturing), and analysis of a deployed mannequin (one making real-world predictions).
In Half 1, I mentioned the method of labelling your picture knowledge that you simply use in your picture classification venture. I confirmed methods to outline “good” photos and create sub-classes. In Half 2, I went over varied knowledge units, past the standard train-validation-test units, equivalent to benchmark units, plus methods to deal with artificial knowledge and duplicate photos.
Analysis of the educated mannequin
As machine studying engineers we take a look at accuracy, F1, log loss, and different metrics to resolve if a mannequin is able to transfer to manufacturing. These are all essential measures, however from my expertise, these scores may be deceiving particularly because the variety of lessons grows.
Though it may be time consuming, I discover it essential to manually evaluation the pictures that the mannequin will get mistaken, in addition to the…
