Decision Trees Tutorial
Master decision tree algorithms from basics to advanced techniques. 5 comprehensive chapters covering introduction, mathematics, Python implementation, overfitting prevention, and ensemble methods.
Chapter 1: Introduction to Decision Trees
Learn the basics of decision trees and how they make decisions
- What are decision trees and how they work
- Tree structure: nodes, branches, leaves
- Real-world examples and applications
- Advantages and limitations
Foundation
Python
Interactive
Chapter 2: Decision Tree Mathematics
Understand entropy, information gain, and splitting criteria
- Entropy and uncertainty measurement
- Information gain calculations
- Gini impurity as alternative
- Interactive calculators and examples
Mathematics
Entropy
Python
Chapter 3: Python Implementation
Build decision trees from scratch with interactive demos
- Python implementation from scratch
- Interactive decision tree building
- Real-world dataset examples
- Performance optimization
Python
Implementation
Interactive
Chapter 4: Overfitting and Pruning
Learn to prevent overfitting with pruning techniques
- Understanding overfitting in trees
- Pre-pruning and post-pruning
- Cross-validation techniques
- Hyperparameter tuning
Overfitting
Pruning
Validation
Chapter 5: Advanced Techniques
Explore ensemble methods and real-world applications
- Random Forest and ensemble methods
- Feature importance analysis
- Real-world case studies
- Best practices and tips
Ensemble
Random Forest
Applications