Feature Engineering Made Easy

The row normalization method

Our final normalization method works row-wise instead of column-wise. Instead of calculating statistics on each column, mean, min, max, and so on, the row normalization technique will ensure that each row of data has a unit norm, meaning that each row will be the same vector length. Imagine if each row of data belonged to an n-dimensional space; each one would have a vector norm, or length. Another way to put it is if we consider every row to be a vector in space:

x = (x₁, x₂, ..., x_n)

Where 1, 2, ..., n in the case of Pima would be 8, 1 for each feature (not including the response), the norm would be calculated as: 

||x|| = √(x₁^{2 +} x₂^{2 +} ... + x_n²)

This is called the L-2 Norm. Other types of norms exist, but ...