On Fri, Oct 23, 2015 at 9:44 AM, Andy <t3k...@gmail.com> wrote: > Hi Ouwen. > I think this looks interesting, and it would be good to have more > non-trivial imputation methods. > > Is anyone familiar with the method? I don't have time to go into the > details of the paper at the moment. >
statsmodels had a GSOC last year to implement MICE, which hasn't been merged yet. For statistics there are two parts to MICE, impute and combine to adjust inference The core is to cycle through all variables, features and dependent, with missing values and impute them based on the other variables, either nearest neighbor or with a full regression model for that variable. This would create "fake" data that would mess up the inference. Inference is based on imputing several times through a cycle that is similar to Gibbs sampling or MCMC but simplified. Then we combine the random imputations to get the results for the model that we are actually interested in. If you don't need inference, then I guess it could be as simple as cycling several times through a nearest neighbor search. Josef > > Adding something like this to sklearn is probably a major undertaking. It > would likely to be a good addition, > but getting it merged may take a lot of effort and patience. > You might want to try tackling an easy issue first to become familiar with > our development practices. > > Cheers, > Andy > > > > On 10/21/2015 05:13 PM, Ouwen Huang wrote: > > Hello all, > > MICE is a recent imputation method that is supported by a package in R. > However, I would like it to be a part of scikit-learn. I see there exists > an imputer that fills in mean, median, and most frequent. Would an added > imputation method 'mice' be acceptable? If so, what are the steps to > creating this addition for scikit-learn (new to the community)? > > MICE reference: http://www.jstatsoft.org/article/view/v045i03/v45i03.pdf > > Best, > Ouwen > > > ------------------------------------------------------------------------------ > > > > _______________________________________________ > Scikit-learn-general mailing > listScikit-learn-general@lists.sourceforge.nethttps://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > > > > ------------------------------------------------------------------------------ > > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > >
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