Binary Quantification
2 papers with code • 0 benchmarks • 0 datasets
Prediction of class prevalence in test samples that may exhibit prior probability shift from training data, in a binary setting.
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Most implemented papers
Multi-Label Quantification
While many quantification methods have been proposed in the past for binary problems and, to a lesser extent, single-label multiclass problems, the multi-label setting (i. e., the scenario in which the classes of interest are not mutually exclusive) remains by and large unexplored.
Binary Quantification and Dataset Shift: An Experimental Investigation
One finding that results from this investigation is that many existing quantification methods that had been found robust to prior probability shift are not necessarily robust to other types of dataset shift.
