Abstract
This paper describes a method for obtaining regression trees using linear regression models in the leaves in a computationally efficient way that allows the use of this method on large data sets. This work is focused on deriving a set of formulae with the goal of allowing an efficient evaluation of all candidate tests that are considered during tree growth.
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© 2002 Springer-Verlag Berlin Heidelberg
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Torgo, L. (2002). Computationally Efficient Linear Regression Trees. In: Jajuga, K., Sokołowski, A., Bock, HH. (eds) Classification, Clustering, and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-56181-8_45
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DOI: https://doi.org/10.1007/978-3-642-56181-8_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43691-1
Online ISBN: 978-3-642-56181-8
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