The train-test split is a method used to estimate how well a machine learning model will perform on new data. It involves:
Splitting your entire dataset into two distinct sets: one for training the model, called the training set, and one for testing the model's performance, called the test set.
You then 'teach' your model using the training set.
After the model has learned from the training data, you test it by making it predict outcomes on the test set. Since you already know the real outcomes for the test set, you can compare them with the model's predictions to see how well it's doing.
The reason for this split is to evaluate the model's ability to generalize to unseen data. If we test on the same data we used for training, we could get overly optimistic results because the model can just memorize the training data. By testing on unseen data, we get a more realistic idea of how the model would perform in the real world.
Typically, you might use about 70-80% of your data for training and the rest for testing.
Updated 5 months ago