An evolutionary estimation procedure for generalized semilinear regression trees

Giulia Vannucci () and Anna Gottard
Additional contact information
Giulia Vannucci: University of Florence
Anna Gottard: University of Florence

Computational Statistics, 2023, vol. 38, issue 4, No 15, 1927-1946

Abstract: Abstract In many applications, the presence of interactions or even mild non-linearities can affect inference and predictions. For that reason, we suggest the use of a class of models laying between statistics and machine learning and we propose a learning procedure. The models combine a linear part and a tree component that is selected via an evolutionary algorithm, and they can be adopted for any kinds of response, such as, for instance, continuous, categorical, ordinal responses, and survival times. They are inherently interpretable but more flexible than standard regression models, as they easily capture non-linear and interaction effects. The proposed genetic-like learning algorithm allows avoiding a greedy search of the tree component. In a simulation study, we show that the proposed approach has a performance comparable with other machine learning algorithms, with a substantial gain in interpretability and transparency, and we illustrate the method on a real data set.

Keywords: Evolutionary algorithms; Interpretable statistical learning; Semi-parametric regression; Regression trunk models (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-022-01302-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:38:y:2023:i:4:d:10.1007_s00180-022-01302-8

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-022-01302-8

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().