Summary of Lifted Coefficient Of Determination: Fast Model-free Prediction Intervals and Likelihood-free Model Comparison, by Daniel Salnikov and Kevin Michalewicz and Dan Leonte
Lifted Coefficient of Determination: Fast model-free prediction intervals and likelihood-free model comparison
by Daniel Salnikov, Kevin Michalewicz, Dan Leonte
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 This paper introduces the “lifted linear model” and derives model-free prediction intervals that become tighter as the correlation between predictions and observations increases. The proposed approach motivates the “Lifted Coefficient of Determination”, a model comparison criterion for arbitrary loss functions in prediction-based settings, such as regression, classification, or counts. The paper also extends the prediction intervals to more general error distributions and proposes a fast model-free outlier detection algorithm for regression. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research makes it possible to predict future events with greater accuracy by considering how well previous predictions match real-world outcomes. The scientists developed a new way to calculate this matching, called the “Lifted Coefficient of Determination”, which helps compare different models and choose the best one for a particular task. They also created an algorithm that quickly identifies unusual data points that don’t fit the usual patterns. |
Keywords
» Artificial intelligence » Classification » Outlier detection » Regression
