Classifier for building 'logistic model trees',
which are classification trees with logistic regression functions at the
leaves. The algorithm can deal with binary and multi-class target variables,
numeric and nominal attributes and missing values.
For more information see:
Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees. Machine
Learning. 95(1-2):161-205.
Marc Sumner, Eibe Frank, Mark Hall: Speeding up Logistic Model Tree
Induction. In: 9th European Conference on Principles and Practice of
Knowledge Discovery in Databases, 675-683, 2005.
BibTeX:
@article{Landwehr2005,
author = {Niels Landwehr and Mark Hall and Eibe Frank},
journal = {Machine Learning},
number = {1-2},
pages = {161-205},
title = {Logistic Model Trees},
volume = {95},
year = {2005}
}
@inproceedings{Sumner2005,
author = {Marc Sumner and Eibe Frank and Mark Hall},
booktitle = {9th European Conference on Principles and Practice of Knowledge Discovery in Databases},
pages = {675-683},
publisher = {Springer},
title = {Speeding up Logistic Model Tree Induction},
year = {2005}
}
Valid options are:
-B
Binary splits (convert nominal attributes to binary ones)
-R
Split on residuals instead of class values
-C
Use cross-validation for boosting at all nodes (i.e., disable heuristic)
-P
Use error on probabilities instead of misclassification error for stopping criterion of LogitBoost.
-I <numIterations>
Set fixed number of iterations for LogitBoost (instead of using cross-validation)
-M <numInstances>
Set minimum number of instances at which a node can be split (default 15)
-W <beta>
Set beta for weight trimming for LogitBoost. Set to 0 (default) for no weight trimming.
-A
The AIC is used to choose the best iteration.
-doNotMakeSplitPointActualValue
Do not make split point actual value.