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Title: On heuristics for learning model trees
Authors: Blockeel, Hendrik ×
Vens, Celine #
Issue Date: 2003
Conference: 4th "Freiburg, Leuven and Friends" Workshop on Machine Learning location:Leuven/Dourbes, Belgium date:March 19-21, 2003
Abstract: Induction of decision trees is a popular learning technique, not
just for classification but also for numerical prediction
(regression). The term "model trees" is commonly used for trees
that in their leaves contain some (usually linear) regression model.
Popular implementations of model tree learners use reduction of
variance as a heuristic for selecting tests during the tree
construction process. In this paper, we show that systems employing
this heuristic may exhibit pathological behaviour in some quite
simple cases. This is not visible in the predictive accuracy of the
tree, but it reduces its explanatory power. We propose an alternative
heuristic that yields equally accurate but simpler trees with better
explanatory power, and this at little or no additional computational
cost.
Publication status: published
KU Leuven publication type: IMa
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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