Getting ready

The act of scaling data is extremely useful. There are a lot of machine learning algorithms, which perform differently (and incorrectly) in the event the features exist at different scales. For example, SVMs perform poorly if the data isn't scaled because they use a distance function in their optimization, which is biased if one feature varies from 0 to 10,000 and the other varies from 0 to 1.

The preprocessing module contains several useful functions for scaling features:

from sklearn import preprocessing
import numpy as np # we'll need it later

Load the Boston dataset:

from sklearn.datasets import load_boston

boston = load_boston()
X,y = boston.data, boston.target