 add regularization terms using the L1 and L2 norm.
The effects are the same as for regression: both push the coefficients towards zero, but the l1 norm
encourages coefficients to be exactly zero, for the same reasons we discussed last time.

You could also use a mixed penalty to get something like the elasticnet. That's not implemented
in the logisticregression class in scikit-learn right now, but it's certainly a sensible thing to do.

Here I used a slightly different notation as last time, though. I'm no