Interpretable classifiers for tabular data via discretization and feature selection
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910608800088064 |
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| author | Jaakkola, Reijo Janhunen, Tomi Kuusisto, Antti Rankooh, Masood Feyzbakhsh Vilander, Miikka |
| author_facet | Jaakkola, Reijo Janhunen, Tomi Kuusisto, Antti Rankooh, Masood Feyzbakhsh Vilander, Miikka |
| contents | We introduce a method for computing immediately human interpretable yet accurate classifiers from tabular data. The classifiers obtained are short Boolean formulas, computed via first discretizing the original data and then using feature selection coupled with a very fast algorithm for producing the best possible Boolean classifier for the setting. We demonstrate the approach via 12 experiments, obtaining results with accuracies comparable to ones obtained via random forests, XGBoost, and existing results for the same datasets in the literature. In most cases, the accuracy of our method is in fact similar to that of the reference methods, even though the main objective of our study is the immediate interpretability of our classifiers. We also prove a new result on the probability that the classifier we obtain from real-life data corresponds to the ideally best classifier with respect to the background distribution the data comes from. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_05680 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Interpretable classifiers for tabular data via discretization and feature selection Jaakkola, Reijo Janhunen, Tomi Kuusisto, Antti Rankooh, Masood Feyzbakhsh Vilander, Miikka Machine Learning Artificial Intelligence Logic in Computer Science I.2.6; F.4.1; I.2.4; E.2 We introduce a method for computing immediately human interpretable yet accurate classifiers from tabular data. The classifiers obtained are short Boolean formulas, computed via first discretizing the original data and then using feature selection coupled with a very fast algorithm for producing the best possible Boolean classifier for the setting. We demonstrate the approach via 12 experiments, obtaining results with accuracies comparable to ones obtained via random forests, XGBoost, and existing results for the same datasets in the literature. In most cases, the accuracy of our method is in fact similar to that of the reference methods, even though the main objective of our study is the immediate interpretability of our classifiers. We also prove a new result on the probability that the classifier we obtain from real-life data corresponds to the ideally best classifier with respect to the background distribution the data comes from. |
| title | Interpretable classifiers for tabular data via discretization and feature selection |
| topic | Machine Learning Artificial Intelligence Logic in Computer Science I.2.6; F.4.1; I.2.4; E.2 |
| url | https://arxiv.org/abs/2402.05680 |