A Step-by-Step Guide to Decision Trees in Machine Learning

Introduction Welcome back, everyone! In this blog post, we will build a Decision Tree model from scratch, explaining each and every step and later testing the model on Breast Cancer dataset. By the end, you’ll have a solid understanding of Decision Trees and how to implement them in code. What is a Decision Tree? A Decision Tree is a type of supervised learning algorithm used for both classification and regression tasks. It works by splitting the data into subsets based on the value of input features, making decisions at each node until reaching a final prediction at the leaf nodes. Lets understand this with the help of a hypothetical scenario. The following diagram illustrates the flow of decision tree for decision making with labels. Rain = Yes No Rain = No How Splitting Happens The way a Decision Tree decides how to split the data involves different techniques: Gini Impurity (for Classification): Think of Gini Impurity as a measure of how mixed up the labels are in a group. If you r