How to normalize close range data?
I use logistic regression. I have some features. Their values are between 0 and 1, (The maximum value that the function can produce is 1 and the minimum value is 0), but both in training and test data the maximum value is very low (e.g. 0.11) therefore all values are low and close to each other. My question is that what is the best standard way to normalize/transfer the feature values to a normal scale (between 0 and 1) so that the logistic regression isn't affected by inappropriate values. Any help would be highly appreciated.
There are different methods for feature scaling/normalization. If you just want the feature values to be in range [0..1] do the following for each feature: Some tutorials recommend to scale features into the range [-0.5 .. 0.5]: I prefer to scale features by their standard deviation how explained in Stanford lectures (see chapter Preprocessing your data):
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