In this post, we will learn about our next machine-learning algorithm, Decision Trees. Imagine you’re trying to decide what movie to watch. You ask simple questions like “Do you want action or comedy?” based on your answers, you keep narrowing down your choices until you find the perfect movie.
That’s exactly how Decision Trees work in computers. They help machines make smart choices by asking simple questions. Let’s dive into the world of Decision Trees and see how they make decisions and how we can use decision trees in machine learning problems.
Table of Contents
Prerequisites
- Linear Algebra For Machine Learning
- Probability And Statistics For Machine Learning
- Python, Numpy, Matplotlib And Pandas
What You Will Learn
- Basic Concepts & Definitions
- Information Theory For ML
- ID3, C4.5, C5.0 and CART algorithms
- CART – Classification and Regression Trees
- Regularization for decision trees
- Python Implementation
- Minimal Cost-Complexity Pruning
- And more
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