Reassessing How to Compare and Improve the Calibration of Machine Learning Models

• URL: http://arxiv.org/abs/2406.04068v1
• Date: Thu, 6 Jun 2024 13:33:45 GMT
• Title: Reassessing How to Compare and Improve the Calibration of Machine Learning Models
• Authors: Muthu Chidambaram, Rong Ge,
• Abstract summary: A machine learning model is calibrated if its predicted probability for an outcome matches the observed frequency for that outcome conditional on the model prediction. We show that there exist trivial recalibration approaches that can appear seemingly state-of-the-art unless calibration and prediction metrics are accompanied by additional generalization metrics.
• Score: 7.183341902583164
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A machine learning model is calibrated if its predicted probability for an outcome matches the observed frequency for that outcome conditional on the model prediction. This property has become increasingly important as the impact of machine learning models has continued to spread to various domains. As a result, there are now a dizzying number of recent papers on measuring and improving the calibration of (specifically deep learning) models. In this work, we reassess the reporting of calibration metrics in the recent literature. We show that there exist trivial recalibration approaches that can appear seemingly state-of-the-art unless calibration and prediction metrics (i.e. test accuracy) are accompanied by additional generalization metrics such as negative log-likelihood. We then derive a calibration-based decomposition of Bregman divergences that can be used to both motivate a choice of calibration metric based on a generalization metric, and to detect trivial calibration. Finally, we apply these ideas to develop a new extension to reliability diagrams that can be used to jointly visualize calibration as well as the estimated generalization error of a model.

Related papers

• Calibrating Large Language Models with Sample Consistency [76.23956851098598]
  We explore the potential of deriving confidence from the distribution of multiple randomly sampled model generations, via three measures of consistency. Results show that consistency-based calibration methods outperform existing post-hoc approaches. We offer practical guidance on choosing suitable consistency metrics for calibration, tailored to the characteristics of various LMs.
  arXiv  Detail & Related papers  (2024-02-21T16:15:20Z)
• Calibration by Distribution Matching: Trainable Kernel Calibration Metrics [56.629245030893685]
  We introduce kernel-based calibration metrics that unify and generalize popular forms of calibration for both classification and regression. These metrics admit differentiable sample estimates, making it easy to incorporate a calibration objective into empirical risk minimization. We provide intuitive mechanisms to tailor calibration metrics to a decision task, and enforce accurate loss estimation and no regret decisions.
  arXiv  Detail & Related papers  (2023-10-31T06:19:40Z)
• Calibration of Neural Networks [77.34726150561087]
  This paper presents a survey of confidence calibration problems in the context of neural networks. We analyze problem statement, calibration definitions, and different approaches to evaluation. Empirical experiments cover various datasets and models, comparing calibration methods according to different criteria.
  arXiv  Detail & Related papers  (2023-03-19T20:27:51Z)
• On Calibrating Semantic Segmentation Models: Analyses and An Algorithm [51.85289816613351]
  We study the problem of semantic segmentation calibration. Model capacity, crop size, multi-scale testing, and prediction correctness have impact on calibration. We propose a simple, unifying, and effective approach, namely selective scaling.
  arXiv  Detail & Related papers  (2022-12-22T22:05:16Z)
• Calibration tests beyond classification [30.616624345970973]
  Most supervised machine learning tasks are subject to irreducible prediction errors. Probabilistic predictive models address this limitation by providing probability distributions that represent a belief over plausible targets. Calibrated models guarantee that the predictions are neither over- nor under-confident.
  arXiv  Detail & Related papers  (2022-10-21T09:49:57Z)
• Variable-Based Calibration for Machine Learning Classifiers [11.9995808096481]
  We introduce the notion of variable-based calibration to characterize calibration properties of a model. We find that models with near-perfect expected calibration error can exhibit significant miscalibration as a function of features of the data.
  arXiv  Detail & Related papers  (2022-09-30T00:49:31Z)
• Calibrate: Interactive Analysis of Probabilistic Model Output [5.444048397001003]
  We present Calibrate, an interactive reliability diagram that is resistant to drawbacks in traditional approaches. We demonstrate the utility of Calibrate through use cases on both real-world and synthetic data.
  arXiv  Detail & Related papers  (2022-07-27T20:01:27Z)
• Modular Conformal Calibration [80.33410096908872]
  We introduce a versatile class of algorithms for recalibration in regression. This framework allows one to transform any regression model into a calibrated probabilistic model. We conduct an empirical study of MCC on 17 regression datasets.
  arXiv  Detail & Related papers  (2022-06-23T03:25:23Z)
• Estimating Expected Calibration Errors [1.52292571922932]
  Uncertainty in probabilistics predictions is a key concern when models are used to support human decision making. Most models are not intrinsically well calibrated, meaning that their decision scores are not consistent with posterior probabilities. We build an empirical procedure to quantify the quality of $ECE$ estimators, and use it to decide which estimator should be used in practice for different settings.
  arXiv  Detail & Related papers  (2021-09-08T08:00:23Z)
• Localized Calibration: Metrics and Recalibration [133.07044916594361]
  We propose a fine-grained calibration metric that spans the gap between fully global and fully individualized calibration. We then introduce a localized recalibration method, LoRe, that improves the LCE better than existing recalibration methods.
  arXiv  Detail & Related papers  (2021-02-22T07:22:12Z)
• Quantile Regularization: Towards Implicit Calibration of Regression Models [30.872605139672086]
  We present a method for calibrating regression models based on a novel quantile regularizer defined as the cumulative KL divergence between two CDFs. We show that the proposed quantile regularizer significantly improves calibration for regression models trained using approaches, such as Dropout VI and Deep Ensembles.
  arXiv  Detail & Related papers  (2020-02-28T16:53:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.