Overfitting is a well-known problem in the ML community. There are methods to avoid this: cross validation, train-test splits, etc. There are also models that g

Overfitting is a well-known problem in the ML community. There are methods to avoid this: cross validation, train-test splits, etc. There are also models that give you an estimate of the standard deviation of a prediction. What is the point? We don't need new algorithms, we just have to apply existing methods properly.