Introduction to Classification & Regression Trees (CART)

A simple example of a decision tree is as follows [Source: Wikipedia]: The main elements of CART (and any decision tree algorithm) are: Rules for splitting data at a node based on the value of one variable; Stopping rules for deciding when a branch is terminal and can be split no more; and Finally, a prediction for the target variable in each terminal node. In addition to maximum tree depth discussed above, stopping rules typically include reaching a certain minimum number of cases in a node, reaching a maximum number of nodes in the tree, etc. Conditions under which further splitting is impossible include when [Source: Handbook of Statistical Analysis and Data Mining Applications by Nisbet et al]: Only one case is left in a node; All other cases are duplicates of each other; and The node is pure (all target values agree). In addition to maximum tree depth discussed above, stopping rules typically include reaching a certain minimum number of cases in a node, reaching a maximum number of nodes in the tree, etc.