Suppose we are reading blood pressure. Some of the readings are corrupt and unusable. We then train a binary classifer to detect high blood pressure. What does the theory of experimental design say about reporting classifier accuracy when some of the data is unusable by our model?
Report classifier accuracy with missing data
Alex
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