Summary of Automated Model Selection For Tabular Data, by Avinash Amballa et al.
Automated Model Selection for Tabular Data
by Avinash Amballa, Gayathri Akkinapalli, Manas Madine, Naga Pavana Priya Yarrabolu, Przemyslaw A. Grabowicz
First submitted to arxiv on: 1 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper proposes a framework for automating model selection in tabular datasets that incorporates feature interactions, aiming to reduce computational costs while maintaining prediction accuracy. The approach involves two methods: Priority-based Random Grid Search and Greedy Search. These methods prioritize features based on their relative importance and iteratively add or remove them to build the optimal solution. The framework is designed for use with R’s mixed effect linear models library and has been demonstrated to effectively capture predictive feature combinations in synthetic experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how we can automatically choose which features are most important when making predictions from datasets. It’s like trying to find the right combination of ingredients to make a delicious cake. The researchers developed two new methods that help us do this by looking at how each ingredient affects the final result. They tested these methods on some fake data and showed that they can be really helpful in finding the best combinations. |
Keywords
* Artificial intelligence * Grid search
