Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models.

Abhineet Agarwal, Yan Shuo Tan, Omer Ronen, Chandan Singh, Bin Yu
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:111-135, 2022.

Abstract

Decision trees and random forests (RF) are a cornerstone of modern machine learning practice. Due to their tendency to overfit, trees are typically regularized by a variety of techniques that modify their structure (e.g. pruning). We introduce Hierarchical Shrinkage (HS), a post-hoc algorithm which regularizes the tree not by altering its structure, but by shrinking the prediction over each leaf toward the sample means over each of its ancestors, with weights depending on a single regularization parameter and the number of samples in each ancestor. Since HS is a post-hoc method, it is extremely fast, compatible with any tree-growing algorithm and can be used synergistically with other regularization techniques. Extensive experiments over a wide variety of real-world datasets show that HS substantially increases the predictive performance of decision trees even when used in conjunction with other regularization techniques. Moreover, we find that applying HS to individual trees in a RF often improves its accuracy and interpretability by simplifying and stabilizing decision boundaries and SHAP values. We further explain HS by showing that it to be equivalent to ridge regression on a basis that is constructed of decision stumps associated to the internal nodes of a tree. All code and models are released in a full-fledged package available on Github

Cite this Paper

BibTeX
@InProceedings{pmlr-v162-agarwal22b, title = {Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models.}, author = {Agarwal, Abhineet and Tan, Yan Shuo and Ronen, Omer and Singh, Chandan and Yu, Bin}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {111--135}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v162/agarwal22b/agarwal22b.pdf}, url = {https://proceedings.mlr.press/v162/agarwal22b.html}, abstract = {Decision trees and random forests (RF) are a cornerstone of modern machine learning practice. Due to their tendency to overfit, trees are typically regularized by a variety of techniques that modify their structure (e.g. pruning). We introduce Hierarchical Shrinkage (HS), a post-hoc algorithm which regularizes the tree not by altering its structure, but by shrinking the prediction over each leaf toward the sample means over each of its ancestors, with weights depending on a single regularization parameter and the number of samples in each ancestor. Since HS is a post-hoc method, it is extremely fast, compatible with any tree-growing algorithm and can be used synergistically with other regularization techniques. Extensive experiments over a wide variety of real-world datasets show that HS substantially increases the predictive performance of decision trees even when used in conjunction with other regularization techniques. Moreover, we find that applying HS to individual trees in a RF often improves its accuracy and interpretability by simplifying and stabilizing decision boundaries and SHAP values. We further explain HS by showing that it to be equivalent to ridge regression on a basis that is constructed of decision stumps associated to the internal nodes of a tree. All code and models are released in a full-fledged package available on Github} }
Endnote
%0 Conference Paper %T Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models. %A Abhineet Agarwal %A Yan Shuo Tan %A Omer Ronen %A Chandan Singh %A Bin Yu %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-agarwal22b %I PMLR %P 111--135 %U https://proceedings.mlr.press/v162/agarwal22b.html %V 162 %X Decision trees and random forests (RF) are a cornerstone of modern machine learning practice. Due to their tendency to overfit, trees are typically regularized by a variety of techniques that modify their structure (e.g. pruning). We introduce Hierarchical Shrinkage (HS), a post-hoc algorithm which regularizes the tree not by altering its structure, but by shrinking the prediction over each leaf toward the sample means over each of its ancestors, with weights depending on a single regularization parameter and the number of samples in each ancestor. Since HS is a post-hoc method, it is extremely fast, compatible with any tree-growing algorithm and can be used synergistically with other regularization techniques. Extensive experiments over a wide variety of real-world datasets show that HS substantially increases the predictive performance of decision trees even when used in conjunction with other regularization techniques. Moreover, we find that applying HS to individual trees in a RF often improves its accuracy and interpretability by simplifying and stabilizing decision boundaries and SHAP values. We further explain HS by showing that it to be equivalent to ridge regression on a basis that is constructed of decision stumps associated to the internal nodes of a tree. All code and models are released in a full-fledged package available on Github
APA
Agarwal, A., Tan, Y.S., Ronen, O., Singh, C. & Yu, B.. (2022). Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models.. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:111-135 Available from https://proceedings.mlr.press/v162/agarwal22b.html.

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