I have a problem, for simplicity let's say it is a binary classification problem.
I am trying to solve this problem using XGBoost.
A standard output plot for any ML algorithm, is the feature importance, and I want to look at the top n features.
But how to decide on n ?
A method would be to add a random variable (a vector with numbers from uniform distribution) in the XGBoost model, and then see in which place this random variable lands, and only look at the variables above the random variable.
If I do that, and then I re-run the model, including only the variables that score above the random one, then the accuracy score in the test set, decreases. Why ?