Machine Learning with Spark - Second Edition

Normalizing features

Once the features have been extracted into the form of a vector, a common preprocessing step is to normalize the numerical data. The idea behind this is to transform each numerical feature in a way that scales it to a standard size. We can perform different kinds of normalization, which are as follows:

• Normalize a feature: This is usually a transformation applied to an individual feature across the dataset, for example, subtracting the mean (centering the feature) or applying the standard normal transformation (such that the feature has a mean of zero and a standard deviation of 1).
• Normalize a feature vector: This is usually a transformation applied to all features in a given row of the dataset such that the resulting ...