During linear regression classes in academia, it is taught that including trivial/irrelevant features to the model decreases its ability to predict more accurately.
In fbprophet, there is this function, add_regressor(), which allows us to add additional regressors to the model.
I want to know: - Is there a way to check whether added parameter/feature actually improves the model or it's actually trivial? - What should I look for during the process of adding regressors to fbprophet?
Of course I prefer a more intuitive, smart way rather than simply checking the evaluation metric with and without the added regressor, since the improvement can simply be noise and cause overfitting, decreasing the ability to generalize on unseen data.