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Supervised machine learning utilizes large datasets, often with ground truthlabels annotated by humans. While some data points are easy to classify, othersare hard to classify, which reduces the inter-annotator agreement. This causesnoise for the classifier and might affect the user's perception of theclassifier's performance. In our research, we investigated whether theclassification difficulty of a data point influences how strongly a predictionmistake reduces the "perceived accuracy". In an experimental online study, 225participants interacted with three fictive classifiers with equal accuracy(73%). The classifiers made prediction mistakes on three different types ofdata points (easy, difficult, impossible). After the interaction, participantsjudged the classifier's accuracy. We found that not all prediction mistakesreduced the perceived accuracy equally. Furthermore, the perceived accuracydiffered significantly from the calculated accuracy. To conclude, accuracy andrelated measures seem unsuitable to represent how users perceive theperformance of classifiers.

How Accurate Does It Feel? -- Human Perception of Different Types of Classification Mistakes