Logistic model tree

In computer science, a logistic model tree (LMT) is a classification model with an associated supervised training algorithm that combines logistic regression (LR) and decision tree learning.

[1] In the logistic variant, the LogitBoost algorithm is used to produce an LR model at every node in the tree; the node is then split using the C4.5 criterion.

Each LogitBoost invocation is warm-started[vague] from its results in the parent node.

[3] The basic LMT induction algorithm uses cross-validation to find a number of LogitBoost iterations that does not overfit the training data.

A faster version has been proposed that uses the Akaike information criterion to control LogitBoost stopping.