Hi There,
I got some results from using nnet on a two-class problem, and I'd like to
hear your comments to understand well about the algorithm. In the training set,
the ratio of class 1 to class 2 is about 23:77. I did a 5-fold cross
validation. The networks were trained twice, one with 'weights=1', one with
'weights=ifelse(species=="class1", 77/33, 1)'(pointed out by Prof. Brian
Ripley).All other settings are same. The average Matthew Correlation Coeffience
for the one with weights=1 is 0.80, significantly larger than that of the
other, 0.74. So, it seems weighting the unbalanced samples does not help
performance on evaluations, which is against my initial thoughts. My question
would be, does that mean the training data is not unbalanced enough? then how
unbalanced is enough? Or it was totally just a signal event? Or it was just
some suboptimal results? Any references regarding this issue in particular?
Thanks!
Best regards,
Baoqiang Cao
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