Tree-structured regression model using a projection pursuit approach

Hyunsun Cho, Eun Kyung Lee

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


In this paper, a new tree-structured regression model—the projection pursuit regression tree—is proposed. It combines the projection pursuit classification tree with the projection pursuit regression. The main advantage of the projection pursuit regression tree is exploring the independent variable space in each range of the dependent variable. Additionally, it retains the main properties of the projection pursuit classification tree. The projection pursuit regression tree provides several methods of assigning values to the final node, which enhances predictability. It shows better performance than CART in most cases and sometimes beats random forest with a single tree. This development makes it possible to find a better explainable model with reasonable predictability.

Original languageEnglish
Article number9885
JournalApplied Sciences (Switzerland)
Issue number21
StatePublished - 1 Nov 2021

Bibliographical note

Funding Information:
Funding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (2018R1A2B6001251).

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • Exploratory data analysis
  • Piecewise regression
  • Projection pursuit
  • Recursive partition
  • Regression tree


Dive into the research topics of 'Tree-structured regression model using a projection pursuit approach'. Together they form a unique fingerprint.

Cite this