3.8 KB · ⇊ 3 parts · ⤓ Micron 3.8 KB · ⤓ HTML 36.8 KB

top

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.cite-ref-ecml03-1-0[1]cite-ref-2[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).cite-ref-ecml03-1-1[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.cite-ref-pkdd05-3-0[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.cite-ref-pkdd05-3-1[3]

• References

• See also

cite-note-ecml03-11. ↑ citerefniels-landwehrmark-halleibe-frank2003Niels Landwehr; Mark Hall; Eibe Frank (2003). Logistic model trees (PDF). ECML PKDD.

cite-note-22. ↑ citereflandwehrhallfrank2005Landwehr, N.; Hall, M.; Frank, E. (2005). "Logistic Model Trees" (PDF). Machine Learning. 59 (1–2): 161–205. doi:10.1007/s10994-005-0466-3.

cite-note-pkdd05-33. ↑ citerefsumner-marceibe-frankmark-hall2005Sumner, Marc; Eibe Frank; Mark Hall (2005). Speeding up logistic model tree induction (PDF). PKDD. Springer. pp. 675–683.

• C4.5 algorithm