Ultimate ML Bootcamp #5: Classification & Regression Trees

Welcome to the fifth chapter of Miuul’s Ultimate ML Bootcamp—a comprehensive series designed to elevate your expertise in machine learning and artificial intelligence. This chapter, Ultimate ML Bootcamp #5: Classification and Regression Trees (CART), builds upon the skills you’ve developed and introduces you to an essential machine learning technique used widely in classification and regression tasks.

In this chapter, we will thoroughly explore the CART methodology. You’ll start by learning the theoretical foundations of how decision trees are constructed, including the mechanisms behind splitting criteria and the strategies for optimizing tree depth.

Moreover, we will delve into various model evaluation metrics specific to CART and explore techniques to prevent overfitting. Practical application of CART in solving real-world problems will be emphasized, with a focus on tuning hyperparameters and assessing feature importance.

This chapter aims to provide a balance of deep theoretical insights and hands-on practical experience, enabling you to implement and optimize CART models effectively. By the end of this exploration, you will be well-equipped with the knowledge to use CART in your own projects and further your journey in machine learning.

We are excited to support your continued learning as you delve into the dynamic world of Classification and Regression Trees. Let’s begin this enlightening chapter and unlock new dimensions of your analytical capabilities!