Summary of Extreme Automl: Analysis Of Classification, Regression, and Nlp Performance, by Edward Ratner et al.
Extreme AutoML: Analysis of Classification, Regression, and NLP Performance
by Edward Ratner, Elliot Farmer, Brandon Warner, Christopher Douglas, Amaury Lendasse
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 research paper focuses on automated machine learning (AutoML), a technique that selects hyperparameters to optimize model performance. The study compares Extreme AutoML, which uses an alternative neural architecture called Extreme Learning Machines (ELMs), with industry leader Google’s AutoML. The authors benchmark these two methods using popular classification datasets from the University of California at Irvine’s (UCI) repository and other data sets. The results show that Extreme AutoML outperforms Google’s AutoML in terms of accuracy, Jaccard Indices, class variance, and training times. This breakthrough has significant implications for cloud-based services like Google AutoML, which is based on Deep Learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to make decisions without needing human help. It compares two different ways that computers can do this: one called Extreme AutoML and another from a company called Google. They tested these methods using lots of data from the University of California at Irvine. The results show that Extreme AutoML is better than Google’s method in many ways, like being more accurate and taking less time to train. |
Keywords
» Artificial intelligence » Classification » Deep learning » Machine learning
