Summary of Negative Impact Of Heavy-tailed Uncertainty and Error Distributions on the Reliability Of Calibration Statistics For Machine Learning Regression Tasks, by Pascal Pernot
Negative impact of heavy-tailed uncertainty and error distributions on the reliability of calibration statistics for machine learning regression tasks
by Pascal Pernot
First submitted to arxiv on: 15 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 In this study, researchers explore two methods for evaluating the average calibration of machine learning regression tasks, specifically focusing on variance-based prediction uncertainties. The first approach involves calculating the calibration error (CE) as the difference between mean absolute error (MSE) and mean variance (MV), while the second method compares mean squared z-scores (ZMS) to 1. However, both methods may yield different conclusions, as demonstrated using datasets from the machine learning uncertainty quantification (ML-UQ) literature. The study finds that estimating MV, MSE, and their confidence intervals becomes unreliable for heavy-tailed uncertainty and error distributions, which are common in ML-UQ datasets. In contrast, the ZMS statistic is less sensitive and provides a more reliable approach. Additionally, the study suggests that conditional calibration statistics, such as ENCE, may also be affected by this issue. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research examines how to accurately evaluate the average calibration of machine learning models. Two methods are discussed: one calculates the difference between mean absolute error (MSE) and mean variance (MV), while the other compares mean squared z-scores (ZMS) to 1. However, both methods might give different results. The study uses datasets from the machine learning uncertainty quantification field to show that estimating MV, MSE, and their confidence intervals becomes tricky when dealing with heavy-tailed data. On the other hand, ZMS is less affected by this issue. This study highlights the importance of carefully evaluating model performance. |
Keywords
* Artificial intelligence * Machine learning * Mse * Regression
