Add early stopping #6
Description
Normally LightGBM supports early stopping, controlled by the early_stopping_round and first_metric_only parameters. The idea is basically to stop training once it stops making progress for some amount of time, and roll back to the round that produced the highest score on validation data (by calling LGBM_BoosterRollbackOneIter the appropriate number of times). This avoids wasting training time, and improves the model by reducing overfitting.
This depends on the metrics of #5.
If #5 implements callbacks for live updates, a minor change to allow the callback to communicate back some decisions would make it possible to implement early stopping entirely as a callback. This is the route taken by tensorflow, and makes it easy to switch in other early stopping techniques.