Data Science and Machine Learning (Theory and Projects) A to Z - Feature Selection: Statistical Based Methods

The video tutorial discusses various feature selection methods, focusing on filter methods that do not rely on machine learning models. It explains the low variance criteria, which eliminates features with low variation, and the T score criteria, used for binary classification to maximize class separation. The Chi-squared score, suitable for multiclass problems, tests feature independence from class labels. Advanced criteria like the Hilbert-Schmidt independence criterion are also mentioned. The tutorial highlights the limitations of statistical methods in handling feature redundancy.

What is the significance of the threshold in the low variance criterion?

What are the limitations of statistical-based feature selection methods?

Discuss the concept of redundancy in feature selection.

How do information theoretical based methods differ from statistical based methods in feature selection?