  Machine learning and deep learning classification models are data-driven, and
the model and the data jointly determine their classification performance. It
is biased to evaluate the model's performance only based on the classifier
accuracy while ignoring the data separability. Sometimes, the model exhibits
excellent accuracy, which might be attributed to its testing on highly
separable data. Most of the current studies on data separability measures are
defined based on the distance between sample points, but this has been
demonstrated to fail in several circumstances. In this paper, we propose a new
separability measure--the rate of separability (RS), which is based on the data
coding rate. We validate its effectiveness as a supplement to the separability
measure by comparing it to four other distance-based measures on synthetic
datasets. Then, we demonstrate the positive correlation between the proposed
measure and recognition accuracy in a multi-task scenario constructed from a
real dataset. Finally, we discuss the methods for evaluating the classification
performance of machine learning and deep learning models considering data
separability.
