A Visual Understanding of Decision Trees and Gradient Boosting | by Reza Bagheri | Jul, 2024

A visual explanation of the math behind decision trees and gradient boosting39 min read·15 hours agoImage generated using DALL.EA decision tree is a non-parametric supervised learning algorithm that can be used for both classification and regression. It uses a tree-like structure to represent decisions and their potential outcomes. Decision trees are simple to understand and interpret and can be easily visualized. However, when a decision tree model becomes too complex, it does not generalize well from the training data and results in overfitting.Gradient boosting is an ensemble learning model in which we combine many weak learners to develop a strong learner. The weak learners are the individual decision trees, and each learner tries to focus on the mistakes of the previous ones. Gradient boosting is typically less prone to overfitting compared to a single deep decision tree.This article will visually explain the intuition behind the decision trees for classification and regression problems. We will see how this model works and why it can result in overfitting. Next, we will introduce gradient boosting and see how it can improve the performance of a single decision tree. A gradient boosting regressor and classifier will be implemented from scratch in Python. Finally, the math…