  Machine learning is more and more applied in critical application areas like
health and driver assistance. To minimize the risk of wrong decisions, in such
applications it is necessary to consider the certainty of a classification to
reject uncertain samples. An established tool for this are reject curves that
visualize the trade-off between the number of rejected samples and
classification performance metrics. We argue that common reject curves are too
abstract and hard to interpret by non-experts. We propose Stacked Confusion
Reject Plots (SCORE) that offer a more intuitive understanding of the used data
and the classifier's behavior. We present example plots on artificial Gaussian
data to document the different options of SCORE and provide the code as a
Python package.
