Here's a trap. If you train a model on some data and then test it on the same data, of course it does well — it has effectively seen the answers. That tells you nothing about how it will cope with the future. So we always split the data into two parts:

• Training set — the examples the model learns from.
• Test set — examples held back, never shown during training, used only to judge the finished model.

The test set is a stand-in for "new data the model has never seen." Performance there is the only honest measure of how good the model really is.

Fit on train, judge on test

The line is fitted using only the training points. Toggle to reveal the test points — held out during training — and see how well the line predicts them. A model that does well on data it never trained on is one you can trust.

The golden rule

Never let the test set leak into training. The moment a model has peeked at the test data — even indirectly, through choices you made — your score is inflated and meaningless. Keeping a clean, untouched test set is the single most important discipline in applied machine learning, and it's what makes the next idea, generalization, measurable at all.