  This is a theoretical paper on "Deep Learning" misconduct in particular and
Post-Selection in general. As far as the author knows, the first peer-reviewed
papers on Deep Learning misconduct are [32], [37], [36]. Regardless of learning
modes, e.g., supervised, reinforcement, adversarial, and evolutional, almost
all machine learning methods (except for a few methods that train a sole
system) are rooted in the same misconduct -- cheating and hiding -- (1)
cheating in the absence of a test and (2) hiding bad-looking data. It was
reasoned in [32], [37], [36] that authors must report at least the average
error of all trained networks, good and bad, on the validation set (called
general cross-validation in this paper). Better, report also five percentage
positions of ranked errors. From the new analysis here, we can see that the
hidden culprit is Post-Selection. This is also true for Post-Selection on
hand-tuned or searched hyperparameters, because they are random, depending on
random observation data. Does cross-validation on data splits rescue
Post-Selections from the Misconducts (1) and (2)? The new result here says: No.
Specifically, this paper reveals that using cross-validation for data splits is
insufficient to exonerate Post-Selections in machine learning. In general,
Post-Selections of statistical learners based on their errors on the validation
set are statistically invalid.
