What are training, validation, and test sets in machine learning?

What are training, validation, and test sets in machine learning?

In machine learning, training sets are used to train the model, validation sets help tune the model's parameters, and test sets evaluate its performance.

Why do we need a validation set if we have a training and test set?

The validation set helps prevent overfitting by allowing you to fine-tune the model's hyperparameters without using the test set.

How do I split my dataset into these sets?

A common approach is to use 70% of the data for training, 15% for validation, and 15% for testing, but this can vary based on the dataset size.

Can I use cross-validation instead of a separate validation set?

Yes, cross-validation is a great technique to assess model performance and can be used instead of a separate validation set.