
Minimising quantifier variance under prior probability shift
For the binary prevalence quantification problem under prior probability...
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Calibrating sufficiently
When probabilistic classifiers are trained and calibrated, the socalled...
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Proving prediction prudence
We study how to perform tests on samples of pairs of observations and pr...
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Confidence intervals for class prevalences under prior probability shift
Point estimation of class prevalences in the presence of data set shift ...
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A plugin approach to maximising precision at the top and recall at the top
For information retrieval and binary classification, we show that precis...
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Fisher consistency for prior probability shift
We introduce Fisher consistency in the sense of unbiasedness as a desira...
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Does quantification without adjustments work?
Classification is the task of predicting the class labels of objects bas...
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Exact fit of simple finite mixture models
How to forecast next year's portfoliowide credit default rate based on ...
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The Law of Total Odds
The law of total probability may be deployed in binary classification ex...
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Dirk Tasche
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