Given we have a binary dependent variable and 100s of features and ~50k observations, is there a generally accepted way to trim the features via some type of machine learning concept? I was trying a Lasso regression to zero out features, but it just showed nothing was significant. I can go through multiple by hand that are definitely significant, though, so I must be doing something wrong. If I had a specific type of selection to look into, I would feel more comfortable learning about that specific concept and knowing it should theoretically work for me.
Sorry I'm a complete noob to this, and am just looking for some general direction.