Decision Trees: A Divide-and-Conquer Approach to Machine Learning

This is the first in a series exploring decision trees in machine learning. Decision trees recursively partition data into regions based on a series of questions, ultimately leading to a prediction. The article clearly explains the mathematical definition of decision trees, the types of decision trees (classification and regression), common algorithms (ID3, C4.5, and CART), and objective functions (Gini impurity, entropy, and squared loss). It also delves into the pros and cons, bias-variance tradeoff, the "staircase effect," and the greedy algorithm used to build decision trees.