Data Science and Machine Learning (Theory and Projects) A to Z - Introduction to Machine Learning: Machine Learning Cros

The video tutorial discusses the validation process, focusing on cross-validation. It explains how cross-validation involves dividing data into partitions to use different sets for training and validation in multiple iterations. The tutorial highlights the benefits of cross-validation, such as improved stability and performance, despite its computational expense. Five-fold cross-validation is used as an example, and the importance of choosing the right number of folds as a hyperparameter is emphasized.

What is the purpose of averaging out the validation loss after multiple iterations?

Discuss the trade-offs involved in using cross-validation compared to a simple validation process.