Sorry I forgot the mention:
Scikit's Logistic Regression is incredibly fast compared to Weka. Weka's
implementation (mostly based on this
paper<http://sci2s.ugr.es/keel/pdf/algorithm/articulo/1992-JSTOR-Cessie-Logistic_Regression.pdf>)
is slow as well as VERY memory intensive. Sometimes it wasn't enough to
allocate *3 GB* as heap size. My dataset (words in above have not more than
100 instance) is very small because I use LR word by word.
Is this the case because scikit's LR uses liblinear library?
Thank you
On Thu, Jan 24, 2013 at 5:25 PM, O. B. <thyme....@gmail.com> wrote:
> Hello all,
>
> I have some problem with my experiments. I used *Logistic Regression (LR)* to
> classify words senses. We have gold tags for (target set) each word
> instance.
>
> I did 10 fold cross validation. Some words in my dataset have more than
> two senses so I wrapped logistic regression with *OneVsRestClassifier.* The
> code is here <http://pastebin.com/c4qvB71A>. Accuracy was not impressive
> and so I suspect if there was an error in my code. So I picked five words
> to classify using LR on Weka. I used default settings on Weka and these are
> the results:
>
> WORD Scikit Weka
>
> - accommodate 0.3 0.667
> - bow 0.05 0.681818
> - display 0.475 0.70
> - haunt 0.575 0.53
> - owe 0.2533 0.4375
>
>
> This are the (correct_label / total_label) scores. Except *haunt, *scores
> are not consistent and scikit's are significantly lower than Weka. I do not
> say scikit has a bug or something, most likely there is a problem in my
> code or Weka makes some pre-processing instead of using raw data directly.
> Could you explain why is there a huge differences between Scikit and Weka
> scores.
>
> Every features have sum to 1 and their values are between 0 and 1.
>
> Attached file contains outputs of Weka if necessary.
>
> Thank you
>
>
>
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