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

The video tutorial explains the importance of dividing data into three partitions: training, validation, and test sets. It highlights the role of the validation set in hyperparameter tuning and model selection, while emphasizing the dangers of data snooping, which occurs when the test set influences model training. To avoid data snooping, the test set should remain untouched until final evaluation. The validation set acts as a proxy for the test set during model tuning, ensuring the test set's purity and preventing overfitting.

What should be done to avoid overfitting during model training?

What is the significance of keeping the test set untouched during model training?

How should the test set be treated during the model evaluation process?