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Found 3 papers, 2 papers with code
Deep Neural Network Benchmarks for Selective Classification
The selective classification framework aims to design a mechanism that balances the fraction of rejected predictions (i. e., the proportion of examples for which the model does not make a prediction) versus the improvement in predictive performance on the selected predictions.
Model Agnostic Explainable Selective Regression via Uncertainty Estimation
Such a framework is known as selective prediction.
AUC-based Selective Classification
We propose a model-agnostic approach to associate a selection function to a given probabilistic binary classifier.
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