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Found 2 papers, 2 papers with code
Cautious Decision-Making for Tree Ensembles
Cautious classifiers are designed to make indeterminate decisions when the uncertainty on the input data or the model output is too high, so as to reduce the risk of making wrong decisions.
Learning Sets of Probabilities Through Ensemble Methods
A possible approach to obtain set-valued predictions is to learn for each query instance a probability set (a. k. a.
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