I now know why the root error is so terrible; it usually predicts neutral,
and many leaves are neutral, but the root is rarely neutral, hence the
terrible error.

This also explains why training the 5-class classifier and using it to
predict binary yes/no worked so poorly; again, many neutral predictions,
even though neutral isn't really an option for hte binary case.

If I had time, I would
1) train it on binary data for binary classification problem
2) Maybe do a fine-grained thing at the end with the root node
to give more weight to it and less to the leaves... although it would be
nice if one model learned everything.

Anyways, I have to move on. Plan: add in regularization, intercept terms,
and try out adagrad before doing the final writeup.