Tree Structured Interpretable Regression

David Lubinsky
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:331-341, 1995.

Abstract

We describe a new method of regression closely related to the regression ideas CART. which has the following potential advantages over traditional methods: the method can naturally be applied to very large datasets in which only a small proportion of the predictors are useful, the resulting regression rules are more easily interpreted and applied, and may be more accurate in application, since the rules are derived by means of a crossvalidation technique which maximizes their predictive accuracy. The system is evaluated in an empirical study and compared to traditional regression and CART systems.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR0-lubinsky95a, title = {Tree Structured Interpretable Regression}, author = {Lubinsky, David}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {331--341}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/lubinsky95a/lubinsky95a.pdf}, url = {https://proceedings.mlr.press/r0/lubinsky95a.html}, abstract = {We describe a new method of regression closely related to the regression ideas CART. which has the following potential advantages over traditional methods: the method can naturally be applied to very large datasets in which only a small proportion of the predictors are useful, the resulting regression rules are more easily interpreted and applied, and may be more accurate in application, since the rules are derived by means of a crossvalidation technique which maximizes their predictive accuracy. The system is evaluated in an empirical study and compared to traditional regression and CART systems.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T Tree Structured Interpretable Regression %A David Lubinsky %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-lubinsky95a %I PMLR %P 331--341 %U https://proceedings.mlr.press/r0/lubinsky95a.html %V R0 %X We describe a new method of regression closely related to the regression ideas CART. which has the following potential advantages over traditional methods: the method can naturally be applied to very large datasets in which only a small proportion of the predictors are useful, the resulting regression rules are more easily interpreted and applied, and may be more accurate in application, since the rules are derived by means of a crossvalidation technique which maximizes their predictive accuracy. The system is evaluated in an empirical study and compared to traditional regression and CART systems. %Z Reissued by PMLR on 01 May 2022.
APA
Lubinsky, D.. (1995). Tree Structured Interpretable Regression. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:331-341 Available from https://proceedings.mlr.press/r0/lubinsky95a.html. Reissued by PMLR on 01 May 2022.

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