medical-13
Classifier calibration
3260 papers • 126 benchmarks • 313 datasets
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).
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in classifier-calibration-27
Trend
Dataset
Best Model
Actions
CIFAR-100
CIFAR-100
Libraries
iUse these libraries to find classifier-calibration-27 models and implementations
Datasets
Subtasks
No subtasks available.