Branching Out: Exploring Tree-Based Models for Regression
Our discussion so far has been anchored around the family of linear models. Each approach, from simple linear regression to penalized techniques like Lasso and Ridge, has offered invaluable insights into predicting continuous outcomes based on linear relationships. As we begin our exploration of tree-based models, it’s important to reiterate that our focus remains on regression. While tree-based models are versatile, how they handle, evaluate, and optimize outcomes differs significantly between classification and regression tasks.
Tree-based regression models are powerful tools in machine learning that can handle non-linear relationships and complex data structures. In this post, we’ll introduce a spectrum of tree-based models, highlighting their strengths and weaknesses. Then, we’ll dive into a practical example of implementing and visualizing