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Found 2 papers, 1 papers with code
Master your Metrics with Calibration
Machine learning models deployed in real-world applications are often evaluated with precision-based metrics such as F1-score or AUC-PR (Area Under the Curve of Precision Recall).
Adversarial confidence and smoothness regularizations for scalable unsupervised discriminative learning
In this paper, we consider a generic probabilistic discriminative learner from the functional viewpoint and argue that, to make it learn well, it is necessary to constrain its hypothesis space to a set of non-trivial piecewise constant functions.
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