SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 21–29 of 29 papers

TitleStatusHype
Hidden Heterogeneity: When to Choose Similarity-Based CalibrationCode0
Classifier Calibration: A survey on how to assess and improve predicted class probabilities—0
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID DataCode0
Classifier Calibration: with application to threat scores in cybersecurityCode0
Better Classifier Calibration for Small Data Sets—0
High Frequency Residual Learning for Multi-Scale Image Classification—0
Binary Classifier Calibration using an Ensemble of Near Isotonic Regression Models—0
Binary Classifier Calibration: Non-parametric approach—0
Binary Classifier Calibration: Bayesian Non-Parametric Approach—0
Show:102550
← PrevPage 3 of 3Next →

No leaderboard results yet.