I thinks its worth mentioning that there are two levels of black box models too. ML can memorize the expected value at each set of variables (at 1 rmp crank wheel rotates at 2 rpm) or it can ‘generalize’ and, for this example, tell us that the wheel rotates at 2x speed of crank. To some extent ‘ML generalization’ provides good ‘out of distribution’ predictions.
I thinks its worth mentioning that there are two levels of black box models too. ML can memorize the expected value at each set of variables (at 1 rmp crank wheel rotates at 2 rpm) or it can ‘generalize’ and, for this example, tell us that the wheel rotates at 2x speed of crank. To some extent ‘ML generalization’ provides good ‘out of distribution’ predictions.