Summary of Policy Trees For Prediction: Interpretable and Adaptive Model Selection For Machine Learning, by Dimitris Bertsimas and Matthew Peroni
Policy Trees for Prediction: Interpretable and Adaptive Model Selection for Machine Learning
by Dimitris Bertsimas, Matthew Peroni
First submitted to arxiv on: 30 May 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 A new methodology is proposed in this paper to address key questions regarding the use of machine learning (ML) models in real-world applications, particularly in high-stakes decision-making. The authors introduce Optimal Predictive-Policy Trees (OP2T), a tree-based approach that yields interpretable policies for adaptively selecting a predictive model or ensemble, and a parameterized option to reject making a prediction. This approach enables interpretable and adaptive model selection and rejection while only assuming access to model outputs. The authors evaluate their method on real-world datasets, including regression and classification tasks with both structured and unstructured data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models are becoming more widely available, but it’s not clear which one is best for a specific task. This paper develops a new way to decide which model to use, called Optimal Predictive-Policy Trees (OP2T). It helps choose the right model and even gives reasons why it’s good or bad. The approach works with different types of data and can be used in real-world applications. |
Keywords
» Artificial intelligence » Classification » Machine learning » Regression
