A Visual Introduction to Machine Learning

A visually-driven introduction to machine learning using an interactive example of classifying homes as being in New York or San Francisco. Covers core concepts including features, classification, decision trees, split points, training vs. test data, and overfitting. The piece walks through how a decision tree is built step by step, from a single elevation boundary to a fully grown tree achieving 100% training accuracy, then demonstrates why overfitting causes poor performance on unseen data.