The accuracy of object recognition has dramatically increased due to deep learning architectures. For example, on the ImageNet benchmark, the top-five classification error rate plummeted from $$28\%$$ in 2010 to $$2.25\%$$ by 2017. Beyond benchmark datasets, this improved visual perception enables complex real-world applications such as diagnosing skin cancer from medical images and identifying birdsong.

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Classification error rate is the most natural alternative. It is defined as the fraction which represents the proportion of the training observations in the region that are not in the most common class:
$$
E=1-\max _{k}\left(\hat{p}_{m k}\right)
$$
where $p_{mk}$ denotes the proportion of training observations in the $m$th region that are from the $k$th class.

*Classification error is not sufficiently sensitive for tree-growing, and in practice the two other measures are preferable.

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