Details
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New Feature
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Status: Closed
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Minor
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Resolution: Won't Do
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None
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None
Description
So far ML has two scalers: min-max and the standard scaler.
A third one frequently used, is the scaler to unit.
We could implement a transformer for this type of scaling for different norms available to the user.
I will make a separate class for the Normalization per sample procedure by using the Transformer API because it is easy to add
it, fit method does nothing in this case.
Scikit-learn has also some calls available outside the Transform API, we might want add that in the future.
These calls work on any axis but they are not re-usable in a pipeline [4]
Right now the existing scalers in Flink ML support per feature normalization by using the Transformer API.
Resources
[1] https://en.wikipedia.org/wiki/Feature_scaling
[2] http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html
[3] https://spark.apache.org/docs/2.1.0/mllib-feature-extraction.html
[4] http://scikit-learn.org/stable/modules/preprocessing.html