Add a new parameter in the main function: outer_scaler #217
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… read a StandardScaler() class. Sometimes we will do some precessing of the data, but we don't want to data displayed in a standarized way -- human cannot understand it! So I fix some functions to solve this problem. At the sametime, this outer_scaler was set as None default, won't create conflict.
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Sorry, I'm a little lost as to the main purpose of having this 'outer_scaler'. If the purpose is just to let users have more control over discretization, wouldn't it be better for them to just discretize the data ahead of time? |
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I think the point here is to present the explanation in terms of the source data (pre-scaling), as opposed to presenting the explanation in terms of the scaled data (post-scaling). |
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It will read a sklearn.preprocessing.StandardScaler() class. Sometimes we will preprocess the data, StandardScaler() is an easy way, but we don't want data displayed in a standardized way -- human cannot understand it! So I modified some functions to solve this problem. At the same time, outer_scaler was set as None default, won't create conflict.