How to split data for better AI models

When you build an AI model, you cannot use all your data at once. You must divide it into three separate piles. The first pile is for training, the second is for tuning, and the third is for final testing.

You use the training set to teach the model. The validation set helps you adjust settings to improve performance. The test set sits untouched until the very end to prove that your model actually works.

Keep these sets completely separate to get honest results. If the model sees the test data during training, it will cheat and give you wrong scores. Clear separation is the best way to ensure your model performs well in the real world.