Decision Trees Made Simple: Machine Learning Explained

Ever wondered how machines make decisions that feel almost human? A lot of classical machine learning models is based on an algorithm known as Decision Trees. One of the most intuitive yet powerful tools in machine learning. They serve as the foundation for many popular algorithms like Random Forests, Bagging, and Boosted Trees, which are widely used in real-world applications such as fraud detection, medical diagnosis, and customer segmentation.

The concept of decision trees was introduced by Leo Breiman, a renowned statistician at the University of California, Berkeley. He proposed a method where data is represented as a branching tree structure: each internal node tests a specific feature or condition, each branch shows the result of that test, and each leaf node makes a final prediction — either a class label (for classification) or a value (for regression).

In fact, when you hear the term CART (Classification and Regression Trees), it’s just another name for decision trees. In this article, we’ll explain how decision trees work, how they’re constructed, and how to implement them in Python using real-world data. In this article, we’ll cover the following modules:

• What are Decision Trees?
• Why Decision Trees?
• Types of Decision Trees
• Key Terminologies
• How to Create a Decision Tree
• Gini Impurity
• Chi-Square
• Information Gain
• Applications of Decision Trees
• The Hyperparameters
• Code Demonstration
• Advantages and Disadvantages of Decision Trees
• Summary and Conclusions