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Master the most interpretable machine learning models. Learn how decision trees recursively partition feature space, choose optimal splits using Gini impurity and entropy, prevent overfitting through pruning, and extract feature importance from learned structures.
Decision trees are among the most intuitive models in machine learning. They mirror human decision-making by asking a series of questions: "Is this feature above a threshold? If yes, go left; if no, go right." This continues until reaching a leaf node that makes the final prediction.
The key advantage of decision trees is interpretability—you can trace exactly why a model made a particular prediction by following the decision path from root to leaf. This makes trees invaluable in domains requiring explainability, like healthcare and finance.
Trees are also remarkably versatile. They handle both numerical and categorical features naturally, don't require feature scaling, and can capture non-linear relationships and interactions without explicit feature engineering. The same algorithm works for classification (predict a class) and regression (predict a continuous value).
The main challenge is overfitting. An unpruned tree will keep splitting until each leaf contains a single training example—perfect training accuracy but useless generalization. Controlling tree complexity through pruning and stopping criteria is essential.
Decision trees also form the foundation for powerful ensemble methods like Random Forests and Gradient Boosting, which combine many trees to achieve state-of-the-art performance.
This chapter covers:
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Recursive partitioning, tree structure, and how trees make predictions — the most interpretable ML model.
Splitting criteria decide each branch, pruning decides when to stop growing
Gini impurity, entropy, and information gain — mathematical measures that guide optimal split selection.
Pre-pruning and post-pruning — controlling tree complexity to prevent overfitting.
Regression trees extend to continuous outputs, feature importance explains the model
Piecewise constant predictions for continuous targets — MSE splitting and the extrapolation problem.
Impurity-based importance, permutation importance, and SHAP — understanding what drives predictions.
Visualizing trees, extracting rules, and interpreting decisions — the unique explainability advantage of trees.
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