Hello scikit!
I need some insights into what I am doing.
Currently I am doing a text classifier (2 classes) using unigrams (word
level) and some writing style features. I am using a Logistic Regression
model, with L1 regularization. I have a decent performance (around .70
f-measure) for the given corpus.
I would like to make an error analysis, that is, to study the incorrectly
classified documents and get some information from them, in order to maybe
develop some rules to treat these cases or improve/modify my features.
I thought about using the values of the coefficients of the fitted
logitequation to get a glimpse of what words in the vocabulary, or
what style
features, affect the most to the classification decision. Is it correct to
assume that if the coefficient of a variable is positive, this means a
higher importance of said variable towards "positive" label? If it is near
to one, is almost 50/50 for the final classification, and if it is
negative, it contributes towards the "negative" class?
I have read about logit regression interpretation (Ref
1<http://www.ats.ucla.edu/stat/mult_pkg/faq/general/odds_ratio.htm>
,Ref 2 <http://www.appstate.edu/~whiteheadjc/service/logit/intro.htm#interp>),
and so it seems this is a correct way to interpret the coefficients, but I
would like to be sure.
If you have any other ideas of how to perform a different error analysis,
please share them with me.
Thanks for the input!
Pavel SORIANO
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