MAPTree: Beating "Optimal" Decision Trees with Bayesian Decision Trees Article Swipe
Colin E. Sullivan
,
Mohit Tiwari
,
Sebastian Thrun
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.15312
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.15312
Decision trees remain one of the most popular machine learning models today, largely due to their out-of-the-box performance and interpretability. In this work, we present a Bayesian approach to decision tree induction via maximum a posteriori inference of a posterior distribution over trees. We first demonstrate a connection between maximum a posteriori inference of decision trees and AND/OR search. Using this connection, we propose an AND/OR search algorithm, dubbed MAPTree, which is able to recover the maximum a posteriori tree. Lastly, we demonstrate the empirical performance of the maximum a posteriori tree both on synthetic data and in real world settings. On 16 real world datasets, MAPTree either outperforms baselines or demonstrates comparable performance but with much smaller trees. On a synthetic dataset, MAPTree also demonstrates greater robustness to noise and better generalization than existing approaches. Finally, MAPTree recovers the maxiumum a posteriori tree faster than existing sampling approaches and, in contrast with those algorithms, is able to provide a certificate of optimality. The code for our experiments is available at https://github.com/ThrunGroup/maptree.
Related Topics
Concepts
Interpretability
Computer science
Maximum a posteriori estimation
Decision tree
Machine learning
A priori and a posteriori
Inference
Bayesian probability
Robustness (evolution)
Artificial intelligence
Incremental decision tree
Bayesian inference
Posterior probability
Decision tree learning
Data mining
Mathematics
Maximum likelihood
Statistics
Chemistry
Philosophy
Biochemistry
Gene
Epistemology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2309.15312
- https://arxiv.org/pdf/2309.15312
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387227465
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387227465Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2309.15312Digital Object Identifier
- Title
-
MAPTree: Beating "Optimal" Decision Trees with Bayesian Decision TreesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-26Full publication date if available
- Authors
-
Colin E. Sullivan, Mohit Tiwari, Sebastian ThrunList of authors in order
- Landing page
-
https://arxiv.org/abs/2309.15312Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2309.15312Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2309.15312Direct OA link when available
- Concepts
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Interpretability, Computer science, Maximum a posteriori estimation, Decision tree, Machine learning, A priori and a posteriori, Inference, Bayesian probability, Robustness (evolution), Artificial intelligence, Incremental decision tree, Bayesian inference, Posterior probability, Decision tree learning, Data mining, Mathematics, Maximum likelihood, Statistics, Chemistry, Philosophy, Biochemistry, Gene, EpistemologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.world | 99, 104 |
| abstract_inverted_index.AND/OR | 57, 65 |
| abstract_inverted_index.better | 131 |
| abstract_inverted_index.dubbed | 68 |
| abstract_inverted_index.either | 107 |
| abstract_inverted_index.faster | 144 |
| abstract_inverted_index.models | 10 |
| abstract_inverted_index.remain | 2 |
| abstract_inverted_index.search | 66 |
| abstract_inverted_index.today, | 11 |
| abstract_inverted_index.trees. | 42, 118 |
| abstract_inverted_index.Lastly, | 80 |
| abstract_inverted_index.MAPTree | 106, 123, 137 |
| abstract_inverted_index.between | 48 |
| abstract_inverted_index.greater | 126 |
| abstract_inverted_index.largely | 12 |
| abstract_inverted_index.machine | 8 |
| abstract_inverted_index.maximum | 33, 49, 76, 88 |
| abstract_inverted_index.popular | 7 |
| abstract_inverted_index.present | 24 |
| abstract_inverted_index.propose | 63 |
| abstract_inverted_index.provide | 158 |
| abstract_inverted_index.recover | 74 |
| abstract_inverted_index.search. | 58 |
| abstract_inverted_index.smaller | 117 |
| abstract_inverted_index.Bayesian | 26 |
| abstract_inverted_index.Decision | 0 |
| abstract_inverted_index.Finally, | 136 |
| abstract_inverted_index.MAPTree, | 69 |
| abstract_inverted_index.approach | 27 |
| abstract_inverted_index.contrast | 151 |
| abstract_inverted_index.dataset, | 122 |
| abstract_inverted_index.decision | 29, 54 |
| abstract_inverted_index.existing | 134, 146 |
| abstract_inverted_index.learning | 9 |
| abstract_inverted_index.maxiumum | 140 |
| abstract_inverted_index.recovers | 138 |
| abstract_inverted_index.sampling | 147 |
| abstract_inverted_index.available | 169 |
| abstract_inverted_index.baselines | 109 |
| abstract_inverted_index.datasets, | 105 |
| abstract_inverted_index.empirical | 84 |
| abstract_inverted_index.induction | 31 |
| abstract_inverted_index.inference | 36, 52 |
| abstract_inverted_index.posterior | 39 |
| abstract_inverted_index.settings. | 100 |
| abstract_inverted_index.synthetic | 94, 121 |
| abstract_inverted_index.algorithm, | 67 |
| abstract_inverted_index.approaches | 148 |
| abstract_inverted_index.comparable | 112 |
| abstract_inverted_index.connection | 47 |
| abstract_inverted_index.posteriori | 35, 51, 78, 90, 142 |
| abstract_inverted_index.robustness | 127 |
| abstract_inverted_index.algorithms, | 154 |
| abstract_inverted_index.approaches. | 135 |
| abstract_inverted_index.certificate | 160 |
| abstract_inverted_index.connection, | 61 |
| abstract_inverted_index.demonstrate | 45, 82 |
| abstract_inverted_index.experiments | 167 |
| abstract_inverted_index.optimality. | 162 |
| abstract_inverted_index.outperforms | 108 |
| abstract_inverted_index.performance | 17, 85, 113 |
| abstract_inverted_index.demonstrates | 111, 125 |
| abstract_inverted_index.distribution | 40 |
| abstract_inverted_index.generalization | 132 |
| abstract_inverted_index.out-of-the-box | 16 |
| abstract_inverted_index.interpretability. | 19 |
| abstract_inverted_index.https://github.com/ThrunGroup/maptree. | 171 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7699999809265137 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |