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Logistic Model Tree

LMT is a classification model with an associated supervised training algorithm that combines logistic regression (LR) and decision tree learning. Logistic model trees are based on the earlier idea of a model tree: a decision tree that has linear regression models at its leaves to provide a piecewise linear regression model (where ordinary decision trees with constants at their leaves would produce a piecewise constant model).

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][2]

Logistic model trees are based on the earlier idea of a model tree: a decision tree that has linear regression models at its leaves to provide a piecewise linear regression model (where ordinary decision trees with constants at their leaves would produce a piecewise constant model).[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 from its results in the parent node. Finally, the tree is pruned.[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 theAkaike information criterion to control LogitBoost stopping.[3]

References[edit]

  1. ^ Jump up to:a b Niels Landwehr, Mark Hall, and Eibe Frank (2003). "Logistic model trees". ECML PKDD.
  2. Jump up^ Landwehr, N.; Hall, M.; Frank, E. (2005). "Logistic Model Trees". Machine Learning 59: 161. doi:10.1007/s10994-005-0466-3. edit
  3. ^ Jump up to:a b Sumner, Marc, Eibe Frank, and Mark Hall (2005). "Speeding up logistic model tree induction". PKDD. Springer. pp. 675–683.

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