Summary of Comparative Evaluation Of Applicability Domain Definition Methods For Regression Models, by Shakir Khurshid et al.
Comparative Evaluation of Applicability Domain Definition Methods for Regression Models
by Shakir Khurshid, Bharath Kumar Loganathan, Matthieu Duvinage
First submitted to arxiv on: 1 Nov 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 tackles the problem of defining the “applicability domain” of predictive models, which refers to the range of data where the model is expected to provide reliable and accurate predictions. The authors argue that this issue is crucial for ensuring the reliability of new predictions. They propose a novel approach based on non-deterministic Bayesian neural networks to define the applicability domain of the model. The method was tested on eight applicability domain detection techniques applied to seven regression models trained on five different datasets, and its performance was benchmarked using a validation framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding the right zone where a prediction model can be trusted to give good answers. It’s important because if you use a model outside this zone, it might give wrong results! The researchers developed a new way to find this zone using special neural networks called Bayesian neural networks. They tested their method on many different models and datasets, and it worked really well! |
Keywords
» Artificial intelligence » Regression
