Classifier uncertainty: evidence, potential impact, and probabilistic treatment

• URL: http://arxiv.org/abs/2006.11105v1
• Date: Fri, 19 Jun 2020 12:49:19 GMT
• Title: Classifier uncertainty: evidence, potential impact, and probabilistic treatment
• Authors: Niklas T\"otsch, Daniel Hoffmann
• Abstract summary: We present an approach to quantify the uncertainty of classification performance metrics based on a probability model of the confusion matrix. We show that uncertainties can be surprisingly large and limit performance evaluation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Classifiers are often tested on relatively small data sets, which should lead to uncertain performance metrics. Nevertheless, these metrics are usually taken at face value. We present an approach to quantify the uncertainty of classification performance metrics, based on a probability model of the confusion matrix. Application of our approach to classifiers from the scientific literature and a classification competition shows that uncertainties can be surprisingly large and limit performance evaluation. In fact, some published classifiers are likely to be misleading. The application of our approach is simple and requires only the confusion matrix. It is agnostic of the underlying classifier. Our method can also be used for the estimation of sample sizes that achieve a desired precision of a performance metric.

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