2012/6/27 Florian Hönig <[email protected]>: > Dear list, > > analogously to sklearn.preprocessing.scale and sklearn.preprocessing.Scaler, > I would like to add something for scaling the individual features to > the interval [0;1]. > > I have encountered a number of datasets where mean/variance scaling didn't > help > much for SVM/SVR, while scaling to [0;1] worked miraculously. > > Would that be appreciated, and if yes, how should I proceed?
Please submit a new pull request for this: http://scikit-learn.org/dev/developers/index.html#contributing-code Don't forget to include some tests and it would be great if you could write an example that compares the outcome of a SVC or SVR model with different scaling (using one of the datasets available by default). > A separate function interval_scale and a separate class IntervalScaler add > redundant code, but I presume that this would preferred to > generalizing the present scale/Scaler, right? I think so, otherwise the constructor parameters will be too complicated to set. > Btw, I think there is a bug in preprocessing.Scaler.fit. As no > transformation should be done > at this point, line 207 in preprocessing.py should be removed: > > inplace_csr_column_scale(X, 1 / self.std_) Please create a dedicated pull request for this with a new test that would failed without you proposed change. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ Live Security Virtual Conference Exclusive live event will cover all the ways today's security and threat landscape has changed and how IT managers can respond. Discussions will include endpoint security, mobile security and the latest in malware threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
