13. Model Evaluation

• Different model metrics are important to think about when you define ths "success" of an AI product, which is why we're reiterating the definitions of precision, recall, and F1 score, here. If you're comfortable with these measurements, you may skip the end of this video.

Why are training and test data separate?

We use training data when a model is learning. In our cat/dog/gerbil example, this is the data a model can use to learn and find patterns that distinguish the different classes of images.

After training, we need a way to test how well a trained model generalizes. The idea is: we want a trained model to perform well on new inputs, that it hasn’t seen before. This is where test data becomes useful! Because a model hasn’t seen it during training, we can test it on this dataset and evaluate its performance.