I think you have the cart before the horse regarding understanding and prediction. Prediction precedes understanding in importance and sometimes methodology, si

I think you have the cart before the horse regarding understanding and prediction. Prediction precedes understanding in importance and sometimes methodology, since if you can't predict outcomes correctly your understanding, however well fleshed-out, will be inaccurate. Interestingly, the parsimony principle is lent empirical weight by bad results obtained from overfitting. Machine learning enables us to multiply many more entities than we can using our conscious thought processes. The various image recognition models use many variables that enable results impossible with other techniques. It does not violate parsimony if you can obtain better results on a large data set. I can predict that if a car drives by it will not teleport to some other location, but will rather continue along its path. I've been able to predict this since I was a child. I did not need to study the laws of motion to do so. I would not take such a prediction with a grain of salt.