Decision Tree Entropy & Gini Impurity : Algorithm step by step | Machine Learning Tutorial

REAL math behind Decision Tree Classifiers? This comprehensive tutorial breaks down everything you need to know about decision trees, including the crucial concepts of Entropy and Gini Impurity that make these algorithms work!

🎯 What You'll Learn:
✅ Complete mathematical foundation of Decision Trees
✅ Entropy calculation with step-by-step examples
✅ Gini Impurity formula and implementation
✅ Information Gain and how it drives tree splitting
✅ When to use Entropy vs Gini Impurity
✅ Real-world examples with calculations
✅ Best practices for building optimal trees

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⏰ Timestamps:
00:00 - Introduction & Overview
00:30 - Fundamentals with Case Studies
03:50 - Mathematical Foundations
06:10 - Entropy IG Implementation
16:15 - Overfitting Prevention & Pruning Techniques
18:00 - Gini Index Implementation
25:30 - Comparison of Entropy & Gini
26:30 - Conclusion & Next Steps

🔥 Perfect for:
Data Science Students
Machine Learning Engineers
Anyone preparing for ML interviews
Developers wanting to understand the math behind algorithms

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