What is Feature Scaling? Explain the different feature scaling techniques
Feature scaling is a data preprocessing technique that involves transforming the numerical values of features to a standardized scale.
Feature scaling is a data preprocessing technique that involves transforming the numerical values of features to a standardized scale.
‘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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