[ML] Correct acceptance criterion for incremental training

[tveasey]

When we decide whether to accept the results of incremental training we compare the loss calculated for the best candidate with the loss calculated for the original model. Since the data summary comprises a subset of the training data we are in effect comparing training error on old data + validation error on new training data with something closer to validation error on all data. If we don't have much new data, or the improvement we can make on it is small, this typically causes us to reject models which actually perform better in test.

This pull request records the gap between the train and validation loss on the old training data and adds it on to the threshold to accept. This is recorded in the model meta data and needs to be added to the configuration parameters when Java calls incremental training. Also it became clear that there is no value in storing the per fold training percentage in the instrumentation since it is fixed for the duration of train and is determined either by user override or from the training data size and it is only needed for the initial train so I just removed it.

[valeriy42]

LGTM. Good job. Just a few comments regarding readability.