Summary of Four Facets Of Forecast Felicity: Calibration, Predictiveness, Randomness and Regret, by Rabanus Derr and Robert C. Williamson
Four Facets of Forecast Felicity: Calibration, Predictiveness, Randomness and Regret
by Rabanus Derr, Robert C. Williamson
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 Machine learning has traditionally focused on losses and regret in evaluating forecasts, but recent interest in calibration has led to a renewed understanding of evaluation methods. This paper shows that calibration and regret are conceptually equivalent, framing the problem as a game between a forecaster, a gambler, and nature. By placing intuitive restrictions on the gamblers and forecasters, calibration and regret naturally emerge from this framework, which also links evaluation to randomness in outcomes. This work highlights the four facets of forecast felicity: predictiveness, randomness, calibration, and regret. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is about making predictions. But how good are those predictions? Traditionally, researchers focused on losses and regret when evaluating forecasts. Now, they’re interested in something called “calibration”. This paper shows that calibration and regret are basically the same thing! They frame the problem as a game where a forecaster makes predictions, a gambler bets on those predictions, and nature decides what happens. By putting some rules in place, they show that calibration and regret come naturally from this game. It also helps us understand how randomness affects our predictions. |
Keywords
* Artificial intelligence * Machine learning
