> Minor quibble, but the proper way is to use validation data to find the best model and training parameters, the test data is only used once to test how well i

> Minor quibble, but the proper way is to use validation data to find the best model and training parameters, the test data is only used once to test how well it actually works. No: I was assuming that at your step, the text with the 'validation data' fails. So, we have to return and use the validation data on each of the fitting efforts. Then to my question: What do we have? Not so easy to answer. Net the two step approach of 'training data' and 'validation data' is not so good. Also we need some assumptions about the two boxes of data and, then, any data we plug into the resulting model. For the self driving cars, once they see something not on their static map of the street, apparently they have to stop and wait, maybe drive around. The point is, those cars can only drive on streets very carefully mapped out, down to 1 cm. Apparently the basic problem is, driving a car takes some basic intelligence, that is, available essentially only from humans. For anything much like real human intelligence, we don't know how to program it. In particular, machine learning doesn't know how to program it.