Step 1: When building a machine learning model, we need to make choices about how the model works. These choices are called hyperparameters.
Step 2: We cannot use the same data to train the model and to check how well it works. This is because we want to see how the model performs on new, unseen data.
Step 3: To do this, we split our data into three parts: training set, validation set, and test set.
Step 4: The training set is used to teach the model. The validation set is used to test different hyperparameters and see which ones work best.
Step 5: After testing different configurations on the validation set, we choose the one that performs the best.
Step 6: Finally, we use the test set to see how well our chosen model works on completely new data.
