TCE: A Test-Based Approach to Measuring Calibration Error

• URL: http://arxiv.org/abs/2306.14343v1
• Date: Sun, 25 Jun 2023 21:12:43 GMT
• Title: TCE: A Test-Based Approach to Measuring Calibration Error
• Authors: Takuo Matsubara, Niek Tax, Richard Mudd, Ido Guy
• Abstract summary: We propose a new metric to measure the calibration error of probabilistic binary classifiers, called test-based calibration error (TCE) TCE incorporates a novel loss function based on a statistical test to examine the extent to which model predictions differ from probabilities estimated from data. We demonstrate properties of TCE through a range of experiments, including multiple real-world imbalanced datasets and ImageNet 1000.
• Score: 7.06037484978289
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes a new metric to measure the calibration error of probabilistic binary classifiers, called test-based calibration error (TCE). TCE incorporates a novel loss function based on a statistical test to examine the extent to which model predictions differ from probabilities estimated from data. It offers (i) a clear interpretation, (ii) a consistent scale that is unaffected by class imbalance, and (iii) an enhanced visual representation with repect to the standard reliability diagram. In addition, we introduce an optimality criterion for the binning procedure of calibration error metrics based on a minimal estimation error of the empirical probabilities. We provide a novel computational algorithm for optimal bins under bin-size constraints. We demonstrate properties of TCE through a range of experiments, including multiple real-world imbalanced datasets and ImageNet 1000.

Related papers

• Calibrating Deep Neural Network using Euclidean Distance [5.675312975435121]
  In machine learning, Focal Loss is commonly used to reduce misclassification rates by emphasizing hard-to-classify samples. High calibration error indicates a misalignment between predicted probabilities and actual outcomes, affecting model reliability. This research introduces a novel loss function called Focal Loss (FCL), designed to improve probability calibration while retaining the advantages of Focal Loss in handling difficult samples.
  arXiv  Detail & Related papers  (2024-10-23T23:06:50Z)
• Beyond Calibration: Assessing the Probabilistic Fit of Neural Regressors via Conditional Congruence [2.2359781747539396]
  Deep networks often suffer from overconfidence and misaligned predictive distributions. We introduce a metric, Conditional Congruence Error (CCE), that uses conditional kernel mean embeddings to estimate the distance between the learned predictive distribution and the empirical, conditional distribution in a dataset. We show that using to measure congruence 1) accurately quantifies misalignment between distributions when the data generating process is known, 2) effectively scales to real-world, high dimensional image regression tasks, and 3) can be used to gauge model reliability on unseen instances.
  arXiv  Detail & Related papers  (2024-05-20T23:30:07Z)
• 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)
• 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)
• T-Cal: An optimal test for the calibration of predictive models [49.11538724574202]
  We consider detecting mis-calibration of predictive models using a finite validation dataset as a hypothesis testing problem. detecting mis-calibration is only possible when the conditional probabilities of the classes are sufficiently smooth functions of the predictions. We propose T-Cal, a minimax test for calibration based on a de-biased plug-in estimator of the $ell$-Expected Error (ECE)
  arXiv  Detail & Related papers  (2022-03-03T16:58:54Z)
• 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)
• Mitigating Bias in Calibration Error Estimation [28.46667300490605]
  We introduce a simulation framework that allows us to empirically show that ECE_bin can systematically underestimate or overestimate the true calibration error. We propose a simple alternative calibration error metric, ECE_sweep, in which the number of bins is chosen to be as large as possible.
  arXiv  Detail & Related papers  (2020-12-15T23:28:06Z)
• Calibration of Neural Networks using Splines [51.42640515410253]
  Measuring calibration error amounts to comparing two empirical distributions. We introduce a binning-free calibration measure inspired by the classical Kolmogorov-Smirnov (KS) statistical test. Our method consistently outperforms existing methods on KS error as well as other commonly used calibration measures.
  arXiv  Detail & Related papers  (2020-06-23T07:18:05Z)

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.