Learning Rate in Gradient Boost How do we decide the learning rate in gradient boost?
I can think of cross validation as one of the methods, but are there any other methods? Also, my teacher said in the class that 0.1 is a good measure for the learning rate (but did not back it with any sources).

It's a hyperparameter, so you pick it by tuning it, usually using cross-validation. There is no one-size-fits-all "good" value of the parameter, as it will be problem-specific. Algorithms like gradient boosting in many cases will let you get away with good-ish results even if you stick to some "default" settings, but by no means they are the optimal ones.
