Chapter 4: Decision Trees
Explore the essentials of Decision Trees in this practical guide. Learn how to apply Decision Trees for classification and regression tasks, focusing on fundamental techniques such as splitting criteria, tree pruning, and overfitting management. This chapter covers the fundamentals of decision tree algorithms, including entropy and information gain, and explains how to build efficient, data-driven models.
Gain hands-on experience with real-world examples and learn how to fine-tune Decision Trees to improve model accuracy and generalization.
Key Takeaways:
- Understand the core principles of Decision Trees and their applications.
- Master tree pruning and splitting techniques to enhance model performance.
- Build robust predictive models using real-world data and examples.
