medical-18
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).
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Benchmarks
These leaderboards are used to track progress in classifier-calibration-37
Trend
Dataset
Best Model
Actions
CIFAR-100
CIFAR-100
Libraries
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Datasets
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Subtasks
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