Machine learning common sense (3): training data split

A train-test split is a model validation process that reveals how your model is performing on new data. One goal of supervised learning is to build a model that performs well on new data. If you have new data, it's best to see how the model performs on it. The problem is that you may not have new data, but you can simulate through processes like train-test splits.

What is a train-test split?
A train-test split is a model validation process that allows you to simulate how your model will perform with new data.