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Technical note: Using model trees for classification
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DOI：10.1023/A:1007421302149.png)
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Abstract
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Model trees, which are a type of decision tree with linear regression functions at the leaves, form the basis of a recent successful technique for predicting continuous numeric values. They can be applied to classification problems by employing a standard method of transforming a classification problem into a problem of function approximation. Surprisingly, using this simple transformation the model tree inducer M5', based on Quinlan's M5, generates more accurate classifiers than the state-of-the-art decision tree learner C5.0, particularly when most of the attributes are numeric.
Keywords:
model trees
classification algorithms
M5
C5.0
decision trees
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