Let’s look at what that means in this case, more concretely, for our dataset. When we first start the training, it’s like we drew a random line through the data. Then as each step of the training progresses, the line moves, step by step, closer to an ideal separation.
Once training is complete, it’s time to see if the model is any good, using Evaluation. This is where that dataset that we set aside earlier comes into play. Evaluation allows us to test our model against data that has never been used for training. This metric allows us to see how the model might perform against data that it has not yet seen. This is meant to be representative of how the model might perform in the real world.
A good rule of thumb I use for a training-evaluation split somewhere on the order of 80/20 or 70/30. Much of this depends on the size of the original source dataset. If you have a lot of data, perhaps you don’t need as big of a fraction for the evaluation dataset.