Summary of Automated Model Selection For Generalized Linear Models, by Benjamin Schwendinger et al.
Automated Model Selection for Generalized Linear Models
by Benjamin Schwendinger, Florian Schwendinger, Laura Vana-Gür
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Optimization and Control (math.OC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces an innovative approach to automate model selection by combining feature subset selection with holistic generalized linear models using mixed-integer conic optimization. The authors directly optimize for model evaluation metrics, such as Akaike and Bayesian information criteria, while addressing multicollinearity issues in the feature selection process. Notably, they propose a novel pairwise correlation constraint that incorporates ideas from Ridge regression and the OSCAR model to ensure effective feature subset selection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using a special kind of math problem (mixed-integer conic optimization) to help choose the best model for a dataset. Right now, choosing the right model can be a tricky process that requires a lot of trial and error. But this new approach can automate the process by considering multiple models at once and selecting the one that performs best. The researchers also come up with a new way to make sure the features they’re using aren’t too similar to each other, which is important for getting good results. |
Keywords
» Artificial intelligence » Feature selection » Optimization » Regression
