Quantifying Performance of a Binary Classifier Through Weight of Evidence

The distributions of the weight of evidence (log Bayes factor) favouring case over noncase status in a test dataset (or test folds generated by cross-validation) can be used to quantify the performance of a diagnostic test (McKeigue (2019), ). The package can be used with any test dataset on which you have observed case-control status and have computed prior and posterior probabilities of case status using a model learned on a training dataset. To quantify how the predictor will behave as a risk stratifier, the quantiles of the distributions of weight of evidence in cases and controls can be calculated and plotted.


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0.6.2 by Marco Colombo, a year ago


Browse source code at https://github.com/cran/wevid

Authors: Paul McKeigue [aut] , Marco Colombo [ctb, cre]

Documentation:   PDF Manual  

GPL-3 license

Imports ggplot2, mclust, pROC, reshape2, zoo

Suggests testthat

See at CRAN