1. Creates sparse matrix for L1 SGD and FTRL
2. Uses the one standard-error rule during cross-validation.
Parameters within one standard error of the lowest-cost solution
are preferred if they are better regularized.
3. Pulls weights matrix for only the features that occurred
in a given batch. In the case of FTRL, this needs to be computed
each on each batch, so the sparsity helps here.