You may also find VectorSlicer and SQLTransformer useful in your case. Just out of curiosity, how would you typically handles categorical features, except for OneHotEncoder.
Regards, Yuhao 2016-07-01 4:00 GMT-07:00 Yanbo Liang <yblia...@gmail.com>: > You can combine the columns which are need to be normalized into a vector > by VectorAssembler and do normalization on it. > Do another assembling for columns should not be normalized. At last, you > can assemble the two vector into one vector as the feature column and feed > it into model training. > > Thanks > Yanbo > > 2016-06-25 21:16 GMT-07:00 段石石 <burness1...@gmail.com>: > >> Hi all: >> >> >> I use the MinMaxScaler for data normalization, but I found the the >> api is only for Vector, we must vectorized the features firtst. However, >> the feature usually include two parts: one is need to be Normalization, >> another should not be normalized such as categorical. I want to add a api >> with the DataFrame which aim to normalize the columns which we want to >> normalize. And then we can make it to be vector and sent to the ML model >> api to train. I think that will be very useful for the developer with >> machine learning. >> >> >> >> Best Regards >> >> Thanks >> > >
