Summary of Interpretable Classifiers For Tabular Data Via Discretization and Feature Selection, by Reijo Jaakkola et al.
Interpretable classifiers for tabular data via discretization and feature selection
by Reijo Jaakkola, Tomi Janhunen, Antti Kuusisto, Masood Feyzbakhsh Rankooh, Miikka Vilander
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method generates short Boolean formulas for tabular data, providing human-interpretable yet accurate classifiers. By first discretizing the data and then applying feature selection and a fast algorithm, the approach produces classifiers comparable in accuracy to random forests, XGBoost, and existing methods on 12 benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers have developed a new way to make computer algorithms more understandable and transparent. They take data from tables and turn it into short formulas that are easy for humans to understand. These formulas can be used to classify things like whether someone is likely to buy a certain product or not. The approach is just as good at getting the right answer as other popular methods, but it’s much easier to see how it works. |
Keywords
* Artificial intelligence * Feature selection * Xgboost
