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? 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? 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_) Best, Florian ------------------------------------------------------------------------------ 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
