I don't think this is an issue directly related to scikit-learn. Your
classifier is learning to always predict the majority class. If you do not
have good training performance, then you either need more data or your
model is in appropriate. You're trying to learn lots of parameters from 100
example
Hi,
I try to apply the MPLclassifier to a subset (100 data points, 2 classes)
of the 20newsgroup dataset. I created (ok, copied) the following pipeline
model_MLP = Pipeline([('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('model_MLP', MLPCla
