Feature selection in machine learning | Full course

Data Science With Marco · Beginner ·📐 ML Fundamentals ·3y ago
Skills: ML Pipelines80%

Key Takeaways

This video course covers feature selection in machine learning using techniques such as variance threshold, filter method, recursive feature elimination (RFE), and the Boruta algorithm, with accompanying code on GitHub.

Original Description

Full source code on GitHub: https://github.com/marcopeix/youtube_tutorials/blob/main/YT_01_feature_selection.ipynb Introduction - 0:00 Initial code setup - 2:19 Variance threshold - 11:04 Variance threshold (code) - 13:02 Filter method - 19:39 Filter method (code) - 21:27 RFE - 29:08 RFE (code) - 30:42 Boruta - 37:12 Boruta (code) - 41:21 Thank you - 46:35 A full course on feature selection in machine learning projects. We first cover a naive method based on variance. Then we move on to filter method and wrapper method like recursive feature elimination or RFE. Finally, implement the Boruta algorithm.
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This video course teaches feature selection techniques in machine learning, including variance threshold, filter method, recursive feature elimination, and Boruta algorithm, with hands-on code implementation.

Key Takeaways
  1. Set up initial code
  2. Apply variance threshold
  3. Implement filter method
  4. Use recursive feature elimination
  5. Apply Boruta algorithm
  6. Compare results and choose best approach
💡 Feature selection is a crucial step in machine learning pipelines, and using techniques like variance threshold, filter method, recursive feature elimination, and Boruta algorithm can significantly improve model performance.

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