Feature Selection for Machine Learning | SelectKBest, Chi‑Square, ANOVA & Mutual Information
In this video, we walk through why feature selection matters in the machine learning lifecycle, how it helps you fight the curse of dimensionality, cut computational cost, prevent overfitting, and make models easier to explain. You’ll learn how filter methods like Chi-Square, ANOVA, and Mutual Information work in practice, and how to implement them step-by-step in Python using scikit-learn’s SelectKBest.
00:00 – What is feature selection?
00:30 – 4 reasons feature selection matters
02:30 – Python example using SelectKBest (scikit-learn)
04:09 – Summary
🗒️ Resources mentioned in the video:
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