[dododo]

if you use some kind of regularization (e.g., if you think the parameters are sparse) it's possible to fit to such a large space without overfitting. this is common in machine learning and statistics (e.g., "N less than p" problems, where N is the number of data and p is the dimensionality of parameterization, common in genomics).

also provided the test data (i.e., the data it's bidding on) is not used in training, this should be a fair(ish) test; overfitting on the training data should lead to poor test performance.

however it's not clear to me that the data aren't being used twice, and what machine learning is actually going on... so in the best of worlds it could be ok, but given the scant details it could be all bunkum...