Title: One-Hot Encode Features With Multiple Labels
Slug: one-hot_encode_features_with_multiple_labels
Summary: How to one-hot encode nominal categorical features with multiple labels per observation 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 MultiLabelBinarizer
import numpy as np

Create Data



In [5]:

    
# Create NumPy array
y = [('Texas', 'Florida'), 
    ('California', 'Alabama'), 
    ('Texas', 'Florida'), 
    ('Delware', 'Florida'), 
    ('Texas', 'Alabama')]

One-hot Encode Data



In [6]:

    
# Create MultiLabelBinarizer object
one_hot = MultiLabelBinarizer()

# One-hot encode data
one_hot.fit_transform(y)









    Out[6]:





array([[0, 0, 0, 1, 1],
       [1, 1, 0, 0, 0],
       [0, 0, 0, 1, 1],
       [0, 0, 1, 1, 0],
       [1, 0, 0, 0, 1]])

View Column Headers



In [7]:

    
# View classes
one_hot.classes_









    Out[7]:





array(['Alabama', 'California', 'Delware', 'Florida', 'Texas'], dtype=object)