Mastering Decision Trees: Handwritten Notes + Code (Beginner to Advanced)
Master Decision Trees with this complete handwritten notes pack covering both Classification and Regression. Inside, you’ll learn: • Decision Tree fundamentals (structure, splits, overfitting) • Entropy with formulas and worked examples • Gini Impurity and comparison with Entropy • Information Gain step-by-step calculation • Pruning techniques and common parameters • Decision Tree as Regressor • Variance Reduction with formula explanation • Ready-to-use Python code for DecisionTreeClassifier and DecisionTreeRegressor These notes combine theory, intuition, math, and implementation in a clean handwritten format — perfect for beginners, college students, interview prep, and quick ML revision. If you want clarity, not confusion, this guide is for you.
Written for
- Course
- Aritifical intelligence and machine learning
Document information
- Uploaded on
- March 2, 2026
- Number of pages
- 6
- Written in
- 2025/2026
- Type
- OTHER
- Person
- Unknown
Subjects
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decision trees
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decision tree classifier
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decision tree regressor
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machine learning notes
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handwritten ml notes
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entropy
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gini impurity
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information gain
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variance reduction
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pruning techniques
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data science no
