# When should I use StandardScaler and when MinMaxScaler?

I have a feature vector with One-Hot-Encoded features and with continous features.

How can I decide now, which data I shall scale with StandardScaler and which data scale with MinMaxScaler? I think I do not have to scale the one-hot-encoded anyway because they are already between 0 and 1.

(I use afterwards a MLPClassifier)

• Rule of thumb: Use StandardScaler for normally distributed data, otherwise use MinMaxScaler. – Simon Larsson Jan 14 '19 at 16:06

One possible preprocessing approach for OneHotEncoding scaling is "soft-binarizing" the dummy variables by converting softb(0) = 0.1, softb(1) = 0.9. From my experience with feedforward Neural Networks this was found to be quite useful, so I expect it to be also benefitial for your MLPClassifier.
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