The Power of Ensemble Learning: How to Use Stacking for Better Machine Learning Models

How do you get the best out of multiple machine learning models? By using stacking! In this tutorial, we explore the power of ensemble learning and why stacking is the ultimate technique for improving machine learning predictions. You'll learn how to combine models like Linear Regression, XGBoost, and Neural Networks to create an even stronger meta model.

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📌 Chapters
00:00 - Introduction to Ensemble Learning and Stacking
00:30 - Why Use Multiple Models Instead of One?
01:03 - The Basics of Model Averaging and Weighting
01:36 - What is Stacking in Machine Learning?
02:05 - Stacking in Regression vs. Classification Problems
02:38 - How K-Fold Cross Validation Works in Stacking
03:08 - Training Base Models Using Cross Validation
04:12 - Generating Predictions for the Meta Model
05:16 - How to Train the Meta Model Using Base Model Outputs
05:51 - Applying the Stacked Model to Test Data
06:24 - Making Predictions with the Meta Model
06:56 - Final Thoughts and Recap on Stacking
07:29 - Next Steps and Where to Learn More

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