Scikit-learn: preprocessing.scale() vs preprocessing.StandardScalar()
Those are doing exactly the same, but:
preprocessing.scale(x)is just a function, which transforms some datapreprocessing.StandardScaler()is a class supporting the Transformer API
I would always use the latter, even if i would not need inverse_transform and co. supported by StandardScaler().
Excerpt from the docs:
The function scale provides a quick and easy way to perform this operation on a single array-like dataset
The preprocessing module further provides a utility class StandardScaler that implements the Transformer API to compute the mean and standard deviation on a training set so as to be able to later reapply the same transformation on the testing set. This class is hence suitable for use in the early steps of a sklearn.pipeline.Pipeline
My understanding is that scale will transform data in min-max range of the data, while standardscaler will transform data in range of [-1, 1].