What is Max Absolute Scaler? Compare it with MinMax Normalization? Why scaling to [-1, 1] might be better than [0, 1] scaling?
‘Max Absolute Scaler’ is another option for preprocessing Training Data.
‘Max Absolute Scaler’ is another option for preprocessing Training Data.
Another technique that we may wish to use, when preparing our ‘Training Data’, is ‘MinMax Normalization’.
Normalization, also known as ‘Unit-Length Scaler’, is a ‘Feature Scaler’ that can be used when preprocessing numerical data.
Feature Standardization is a technique for pre-processing numerical raw data during the creation of your training data.
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