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.

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