Summary of Scoring Rule Nets: Beyond Mean Target Prediction in Multivariate Regression, by Daan Roordink and Sibylle Hess
Scoring rule nets: beyond mean target prediction in multivariate regression
by Daan Roordink, Sibylle Hess
First submitted to arxiv on: 22 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 probabilistic regression model trained with maximum likelihood estimation can sometimes overestimate variance in the multivariate domain. The Continuous Ranked Probability Score is a popular choice for univariate models, but lacks an equivalent alternative for multivariate models. To address this, we propose Conditional CRPS, a strictly proper scoring rule that extends CRPS to the multivariate case. We demonstrate closed-form expressions for popular distributions and show sensitivity to correlation between target variables. Experimental results on synthetic and real data suggest that Conditional CRPS often outperforms MLE and produces comparable results to state-of-the-art non-parametric models like Distributional Random Forest. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers found a problem with some math models used in science. These models, called probabilistic regression models, can sometimes make wrong predictions by guessing too much information. This is a big issue when working with lots of variables at once. They tried to find a better way to evaluate these models, but it’s hard because there isn’t a clear alternative to the current method. The researchers came up with a new idea called Conditional CRPS that can fix this problem. They showed that their new method works well for different types of data and is even as good as more advanced methods. |
Keywords
» Artificial intelligence » Likelihood » Probability » Random forest » Regression
