Title: Imputing Missing Class Labels
Slug: imputing_missing_class_labels
Summary: How to impute missing class labels for machine learning in Python.
Date: 2016-09-06 12:00
Category: Machine Learning
Tags: Preprocessing Structured Data
Authors: Chris Albon

Preliminaries



In [1]:

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

Create Feature Matrix With Missing Values



In [4]:

    
# Create feature matrix with categorical feature
X = np.array([[0, 2.10, 1.45], 
              [1, 1.18, 1.33], 
              [0, 1.22, 1.27],
              [0, -0.21, -1.19],
              [np.nan, 0.87, 1.31],
              [np.nan, -0.67, -0.22]])

Fill Missing Values' Class With Most Frequent Class



In [5]:

    
# Create Imputer object
imputer = Imputer(strategy='most_frequent', axis=0)

# Fill missing values with most frequent class
imputer.fit_transform(X)









    Out[5]:





array([[ 0.  ,  2.1 ,  1.45],
       [ 1.  ,  1.18,  1.33],
       [ 0.  ,  1.22,  1.27],
       [ 0.  , -0.21, -1.19],
       [ 0.  ,  0.87,  1.31],
       [ 0.  , -0.67, -0.22]])