Summary of Optimizing Estimators Of Squared Calibration Errors in Classification, by Sebastian G. Gruber et al.
Optimizing Estimators of Squared Calibration Errors in Classification
by Sebastian G. Gruber, Francis Bach
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 mean-squared error-based risk to compare and optimize different estimators for squared calibration errors in practical settings. The goal is to improve the calibration of machine learning models, which is crucial for trustworthiness and interpretability, especially in sensitive decision-making scenarios. The reformulation of calibration estimation as a regression problem allows quantification of estimator performance, including canonical calibration. A training-validation-testing pipeline is advocated for estimating calibration errors on evaluation datasets. This approach optimizes existing estimators and compares them to novel kernel ridge regression-based estimators on standard image classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better machine learning models by choosing the right way to measure how good they are at predicting correct answers. Right now, there are many ways to do this, but it’s not clear which one is best. The authors of this paper came up with a new idea for measuring how well these methods work. They tested their idea on some common image recognition tasks and showed that it can help us make better models. |
Keywords
» Artificial intelligence » Image classification » Machine learning » Regression
