Decision Trees

A tree chooses feature tests that divide training examples, continuing recursively until a stopping rule is met. Leaves store a prediction, such as a class distribution or mean response. Split criteria quantify improvement in impurity or loss. Depth, leaf size and pruning control how finely the space is partitioned. A tree represents interactions through successive conditions and can model nonlinear relationships without a global equation. The readable structure is useful for inspection, but the fitting process is greedy in common implementations and small data changes can alter early splits, producing a substantially different tree.

Choose an objective and verify feature types and missing-value behavior in the implementation. Constrain depth, minimum leaf size or pruning using a valid development procedure. Inspect representative decision paths and compare them with domain expectations, then test performance and path stability on held-out data. Examine whether a split uses a genuine predictor or a proxy for information unavailable at prediction time. The deliverable combines the tree or a simplified representation with evidence that its apparent interpretability corresponds to a useful and sufficiently stable model.

Trees can be unstable, and a deep tree may memorize training examples. Axis-aligned splits can require many branches for smooth or oblique relationships. Leaf probabilities based on few examples can be unreliable. A decision path explains the model's rule, not the cause of the real-world outcome. Trees used inside forests or boosting are components of an ensemble; reading one component does not explain the ensemble's complete behavior. Validate complexity and stability rather than equating a visible rule set with trustworthiness.