Title: Normalizing Observations
Slug: normalizing_observations
Summary: How to normalize observations for machine learning in Python.
Date: 2016-09-06 12:00
Category: Machine Learning
Tags: Preprocessing Structured Data
Authors: Chris Albon

Preliminaries



In [4]:

    
# Load libraries
from sklearn.preprocessing import Normalizer
import numpy as np

Create Feature Matrix



In [5]:

    
# Create feature matrix
X = np.array([[0.5, 0.5], 
              [1.1, 3.4], 
              [1.5, 20.2], 
              [1.63, 34.4], 
              [10.9, 3.3]])

Normalize Observations

Normalizer rescales the values on individual observations to have unit norm (the sum of their lengths is one).



In [6]:

    
# Create normalizer
normalizer = Normalizer(norm='l2')

# Transform feature matrix
normalizer.transform(X)









    Out[6]:





array([[ 0.70710678,  0.70710678],
       [ 0.30782029,  0.95144452],
       [ 0.07405353,  0.99725427],
       [ 0.04733062,  0.99887928],
       [ 0.95709822,  0.28976368]])