Summary of Regression Trees For Fast and Adaptive Prediction Intervals, by Luben M. C. Cabezas et al.
Regression Trees for Fast and Adaptive Prediction Intervals
by Luben M. C. Cabezas, Mateus P. Otto, Rafael Izbicki, Rafael B. Stern
First submitted to arxiv on: 12 Feb 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 proposes a new family of model-agnostic methods for calibrating prediction intervals in regression problems, achieving local coverage guarantees. The approach is based on creating the coarsest partition of the feature space that approximates conditional coverage, trained on conformity scores from regression trees and Random Forests. This method demonstrates superior scalability and performance compared to established baselines in simulated and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better predictions by showing how to add uncertainty to our predictions. Right now, predictive models can be wrong, but we don’t know how wrong they are. The authors want to change this by creating a way to get accurate prediction ranges. They do this by looking at the data and finding the best way to group it together. This new method works well in different situations and is fast, making it useful for many types of predictions. |
Keywords
* Artificial intelligence * Regression
