A classification tree is a model that helps us see the structure within the chaos, a decision-making map dressed in the garb of logic and statistics, yet beating with the intuitive heart of storytelling. At its core, a classification tree is a type of decision tree used in machine learning and statistics to categorize data into distinct groups or classes. Imagine it as a tree that grows downward, where:
- The root begins with a question—a choice based on the data’s features.
- The branches represent possible answers or outcomes to that question.
- The leaves are the final classifications—the predicted class labels.
Each decision made at a node is typically based on a variable that best splits the data according to some metric (like Gini impurity, entropy, or information gain). The goal? To purify each branch, grouping similar outcomes together until we reach certainty—or as close as data allows.