Summary of Confidence Interval Estimation Of Predictive Performance in the Context Of Automl, by Konstantinos Paraschakis et al.
Confidence Interval Estimation of Predictive Performance in the Context of AutoML
by Konstantinos Paraschakis, Andrea Castellani, Giorgos Borboudakis, Ioannis Tsamardinos
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
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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 presents a comparative evaluation of 9 state-of-the-art methods for estimating confidence intervals (CIs) in an AutoML setting. This is crucial because traditional point estimates can be misleading due to the “winner’s curse” effect. The authors compare the methods on a corpus of real and simulated datasets, considering inclusion percentage, CI tightness, and execution time as metrics. They find that BBC-F, a variant of the Bootstrap Bias Correction method, outperforms other methods in all evaluated metrics. This work extends previous research to imbalanced and small-sample tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper compares different ways to predict how well a machine learning model will do on new data. It’s important to know not just how good the model is now, but also how sure you are of that prediction. The authors test 9 different methods for doing this, using real and fake datasets. They look at three things: does the method give correct answers most of the time? Are the predictions precise or vague? And how long does it take to get the answer? They find one method, called BBC-F, works best in all these areas. |
Keywords
» Artificial intelligence » Machine learning
