although we cannot look into the futurewe can and should simulate similar effect by holding out part of our data let us removefor instancea certain percentage of the data and train on the remaining one then we use the hold-out data to calculate the error as the model has been trained not knowing the hold-out datawe should get more realistic picture of how the model will behave in the future the test errors for the models trained only on the time after the inflection point now show completely different picture error = , , error = , , error = , , error = , , error = , , the result can be seen in the following chartit seems we finally have clear winner the model with degree has the lowest test errorwhich is the error when measured using data that the model did not see during training and this is what lets us trust that we won' get bad surprises when future data arrives