For tradeoffs you design your error metric to be whatever best represents your problem. >And if the test fails, then try to fit again. Run all night and in th

For tradeoffs you design your error metric to be whatever best represents your problem. >And if the test fails, then try to fit again. Run all night and in the morning, presto, have a model that does well on the test data. Now what have we? Not a trivial question to answer. 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. >A little like 'self driving cars': Really 'self-driving'? Nope, not close. Only on streets where everything has been mapped down to 1 cm or better including all the painted lines, all the curbs, all the traffic signs, and all the traffic lights, etc. In no way does the 'self driving car' actually look at a new street scene, make sense out of it, and use that 'learning' to drive. Instead, so far a self driving car is about as amazing as a train on a track. That's not accurate. Self driving cars do take input and can't rely on a static map since the world changes and it has to detect obstacles and other cars. I'm not entirely certain but I don't believe they are using very much machine learning in current generation self-driving cars. However there is a startup working to make one with pure machine vision, and it was done once in the '90s with neural nets (ALVINN.)