Summary of Regal: Python Package For Active Learning Of Regression Problems, by Elizaveta Surzhikova and Jonny Proppe
regAL: Python Package for Active Learning of Regression Problems
by Elizaveta Surzhikova, Jonny Proppe
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Software Engineering (cs.SE)
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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 paper, researchers aim to develop machine learning models that can accelerate discovery in various fields like (bio)chemistry, materials science, or medicine without requiring large datasets. They propose an active learning method that estimates the model’s knowledge about certain regions of the application domain to guide the choice of training set. This approach is particularly useful for regression problems with continuous outcomes. To facilitate this process, they present a Python package called regAL, which allows users to evaluate different active learning strategies and provides additional customization options. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models are crucial in many fields, but often require large datasets that can be expensive or difficult to obtain. Researchers have developed an active learning method to overcome this obstacle. This method helps develop machine learning models with a desired performance while requiring the least possible number of computational or experimental results from the domain of application. The paper presents a Python package called regAL, which makes it easy for anyone to perform and understand active learning in their specific problem scope. |
Keywords
» Artificial intelligence » Active learning » Machine learning » Regression
