Hi All,
Given a classification problem, when trying to determine what input parameters to include in a model, is it generally better to include more parameters at the risk of some or many of them, possibly, not having any importance? Or, include fewer at the risk of possibly missing an important input? How detrimental to training, if at all, would it be to have hundreds of parameters if only a handful are relevant?
Thanks!