Classification and Regression Trees.¶

Why this mattered¶

TBD

(no abstract available)

enables → Regression Shrinkage and Selection Via the Lasso — CART's tree-based variable selection highlighted sparse predictive modeling, a goal lasso pursued with convex L1-penalized regression.
enables → Greedy function approximation: A gradient boosting machine. — CART supplied regression trees as flexible base learners, enabling gradient boosting machines to combine many trees through additive optimization.
enables → Mining association rules between sets of items in large databases — CART's recursive partitioning of discrete attributes helped frame market-basket data as rule-discoverable itemset splits.
cite ← Regression Shrinkage and Selection Via the Lasso — The lasso cites CART as an alternative regression and classification method that performs variable selection through recursive partitioning.
cite ← Greedy function approximation: A gradient boosting machine. — Friedman's gradient boosting uses regression and classification trees as weak learners in an additive ensemble.
cite ← Mining association rules between sets of items in large databases — Association-rule mining relates to CART because both extract predictive or descriptive rules from tabular item-attribute data.