I found that after training on 98 freki files the DictVectorizer used by models.py doesn't contain the following features:
L-LMm
G-overlap
W-prevclass
This means that these features are not used in training or testing and this may in part account for the low performance. Currently I'm not sure why these features are not making it into the vectors.
I found that after training on 98 freki files the DictVectorizer used by models.py doesn't contain the following features:
L-LMm
G-overlap
W-prevclass
This means that these features are not used in training or testing and this may in part account for the low performance. Currently I'm not sure why these features are not making it into the vectors.