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Classifier calibration

Confidence calibration – the problem of predicting probability estimates representative of the true correctness likelihood – is important for classification models in many applications. The two common calibration metrics are Expected Calibration Error (ECE) and Maximum Calibration Error (MCE).

Papers

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Showing 11–20 of 29 papers

TitleStatusHype
What is Your Metric Telling You? Evaluating Classifier Calibration under Context-Specific Definitions of Reliability—0
Better Classifier Calibration for Small Data Sets—0
Binary Classifier Calibration: Bayesian Non-Parametric Approach—0
Binary Classifier Calibration: Non-parametric approach—0
Binary Classifier Calibration using an Ensemble of Near Isotonic Regression Models—0
Classifier Calibration: A survey on how to assess and improve predicted class probabilities—0
Classifier Calibration with ROC-Regularized Isotonic Regression—0
Decoupling Decision-Making in Fraud Prevention through Classifier Calibration for Business Logic Action—0
FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning—0
Improved User Identification through Calibrated Monte-Carlo DropoutCode0
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