Classifier uncertainty: evidence, potential impact, and probabilistic treatment
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 c...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2021-03-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-398.pdf |