Summary of Learned Feature Importance Scores For Automated Feature Engineering, by Yihe Dong et al.
Learned Feature Importance Scores for Automated Feature Engineering
by Yihe Dong, Sercan Arik, Nathanael Yoder, Tomas Pfister
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this research paper, the authors propose an automated feature engineering framework called AutoMAN, which aims to relieve manual effort and improve model performance by effectively exploring the candidate transforms space. AutoMAN learns feature transform importance end-to-end, incorporating a dataset’s task target directly into feature engineering, resulting in state-of-the-art performance with significantly lower latency compared to alternatives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AutoMAN is an automated machine learning framework that helps relieve manual effort and improve model performance by automatically generating features from existing data. It works by learning which transformations are most important for each data set and task. This approach allows for faster and more accurate feature engineering, making it a valuable tool for machine learning practitioners. |
Keywords
» Artificial intelligence » Feature engineering » Machine learning
