3 Greedy Algorithms for Decision Trees, Explained with Examples

Decision trees are flowchart-like models used for classification and regression that find optimal split points to categorize data. These models aim to maximize the purity of resulting groups by using impurity measures like Entropy or Gini Impurity. The process utilizes greedy algorithms to find the best splits, with three main types being the Exact Greedy, Approximate Greedy, and Histogram-based algorithms. A walkthrough example predicting customer subscriptions demonstrates how each algorithm computes gains to determine the most effective feature and split point for the model.