Born-again tree ensembles. Vidal, T. & Schiffer, M. In III, H. D. & Singh, A., editors, Proceedings of the 37th International Conference on Machine Learning, volume 119, of Proceedings of Machine Learning Research, pages 9743–9753, Virtual, 2020. PMLR.
Born-again tree ensembles [link]Paper  abstract   bibtex   
The use of machine learning algorithms in finance, medicine, and criminal justice can deeply impact human lives. As a consequence, research into interpretable machine learning has rapidly grown in an attempt to better control and fix possible sources of mistakes and biases. Tree ensembles, in particular, offer a good prediction quality in various domains, but the concurrent use of multiple trees reduces the interpretability of the ensemble. Against this background, we study born-again tree ensembles, i.e., the process of constructing a single decision tree of minimum size that reproduces the exact same behavior as a given tree ensemble in its entire feature space. To find such a tree, we develop a dynamic-programming based algorithm that exploits sophisticated pruning and bounding rules to reduce the number of recursive calls. This algorithm generates optimal born-again trees for many datasets of practical interest, leading to classifiers which are typically simpler and more interpretable without any other form of compromise.
@inproceedings{Vidal2020a,
abstract = {The use of machine learning algorithms in finance, medicine, and criminal justice can deeply impact human lives. As a consequence, research into interpretable machine learning has rapidly grown in an attempt to better control and fix possible sources of mistakes and biases. Tree ensembles, in particular, offer a good prediction quality in various domains, but the concurrent use of multiple trees reduces the interpretability of the ensemble. Against this background, we study born-again tree ensembles, i.e., the process of constructing a single decision tree of minimum size that reproduces the exact same behavior as a given tree ensemble in its entire feature space. To find such a tree, we develop a dynamic-programming based algorithm that exploits sophisticated pruning and bounding rules to reduce the number of recursive calls. This algorithm generates optimal born-again trees for many datasets of practical interest, leading to classifiers which are typically simpler and more interpretable without any other form of compromise.},
address = {Virtual},
author = {Vidal, T. and Schiffer, M.},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
editor = {III, Hal Daum{\'{e}} and Singh, Aarti},
file = {:C$\backslash$:/Users/Thibaut/Documents/Mendeley-Articles/Vidal, Schiffer/Vidal, Schiffer - 2020 - Born-again tree ensembles.pdf:pdf},
mendeley-groups = {Machine Learning/Decision Trees/Misc},
pages = {9743--9753},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
title = {{Born-again tree ensembles}},
url = {http://proceedings.mlr.press/v119/vidal20a.html},
volume = {119},
year = {2020}
}

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