Label Encoder vs One Hot Encoder in Machine Learning

Think of a real-estate dataset with categories like "neighborhood A" and "neighborhood B." Label Encoding assigns a number to each, while One-Hot Encoding creates separate columns for each category. 

Choosing the wrong encoding method in machine learning can lead to misleading results and poor model accuracy.

This article will help you understand the difference between Label Encoder vs One Hot Encoder and guide you in making the right choice for your data.

Label Encoder vs One Hot Encoder: Key Differences

In a recommendation system, like the ones used by Netflix or Amazon, categorical data such as movie genres or customer preferences need to be converted into a machine-readable format. This is where encoding techniques like Label Encoder and One Hot Encoder come into play. 

While Label Encoder assigns numbers to categories, One Hot Encoder creates separate binary columns for each category.

Handling machine learning models isn’t just about selecting the right algorithm. You need the right data preprocessing techniques, like Label Encoder and One Hot Encoder. Here are three programs that can help you:

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To help you better understand how these technologies differ, check out the table below.

Also Read: Decision Tree vs Random Forest: Use Cases & Performance Metrics

Using Label Encoding for categories like "Electronics", "Clothing", and "Furniture" might make the model mistakenly treat them as ordered. But with One Hot Encoding, each category gets its own binary column, ensuring the model treats them equally, avoiding any incorrect assumptions about their relationship. 

Choosing the right encoding method leads to more accurate predictions.

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Next, let’s take a quick look at what label encoders and one-hot encoders are, and how they function in machine learning. 

A Quick Guide to Label Encoder and One Hot Encoder

Let's say you need to analyze customer data such as preferred product categories like "electronics," "clothing," or "furniture." To process this data, you’ll need to convert these categories into numerical values. Label Encoder assigns each category a unique integer, while One Hot Encoder creates separate binary columns for each category. 

To fully understand the differences between Label Encoder vs One Hot Encoder, it’s essential to grasp the fundamentals of both techniques.

What is Label Encoder?

Label encoding is a method that converts these categories into numbers so that algorithms can process them. It’s especially useful when your data has an inherent order, like "low," "medium," and "high."

There are two types of categorical data you’ll come across: ordinal and nominal. 

Ordinal data has a natural order (e.g., "low," "medium," "high"), while nominal data doesn’t (e.g., "red," "blue," "green"). For nominal data, you can’t simply assign numbers like you can with ordinal data.

Label Encoding works by assigning a unique integer to each category in your data. Here's a simple breakdown of the process:

1. Each category is mapped to a distinct integer: Label Encoding doesn’t worry about whether one category is more important than another, it simply converts the categories to numerical values.
2. For example, if you have a list of colors like ["Red", "Blue", "Green"], Label Encoding will assign a number to each color in the order it appears, resulting in something like:
   • "Red" = 0
   • "Blue" = 1
   • "Green" = 2

This is done to ensure that machine learning algorithms can process categorical features and make predictions.

Visual Representation

Label Encoding is effective in models that can handle ordinal relationships or models that don’t interpret the value as representing a rank or order. Common models include:

• Decision Trees
• Random Forests
• Gradient Boosting Machines (GBM)

These models can work with integers as input, and since they don't treat the values as having a specific order (e.g., 0 is not "less than" 1 or "more than" 2 in decision trees), Label Encoding works fine.

Also Read: Top 10 Data Science Algorithms Every Data Scientist Should Know

Suppose you are working on a dataset from a ride-sharing company, and you need to predict the type of ride a customer will request based on their location. The "Ride Type" column includes values like "Economy," "Premium," and "Luxury." 

You need to encode these categories as numbers for use in a machine learning model.  

# Importing necessary libraries
from sklearn.preprocessing import LabelEncoder

# Sample dataset with ride type preferences
ride_types = ['Economy', 'Premium', 'Luxury', 'Economy', 'Luxury', 'Premium', 'Economy']

# Creating the LabelEncoder object
label_encoder = LabelEncoder()

# Fitting the LabelEncoder and transforming the data
encoded_ride_types = label_encoder.fit_transform(ride_types)

# Displaying the result
print("Encoded Ride Types:", encoded_ride_types)
print("Classes (Original Categories):", label_encoder.classes_)

Output: 

Encoded Ride Types: [1 2 0 1 0 2 1]
Classes (Original Categories): ['Economy' 'Luxury' 'Premium']

Explanation:

• Encoded Ride Types:
  The LabelEncoder has assigned integer values to each of the categories:

  • "Economy" = 1
  • "Premium" = 2
  • "Luxury" = 0

  The resulting encoded values are:

  • "Economy" is replaced by 1,
  • "Premium" is replaced by 2,
  • "Luxury" is replaced by 0.
• Classes:
  The label_encoder.classes_ attribute shows the mapping of the original categorical values to the numerical values.
  • "Economy" = 1
  • "Premium" = 2
  • "Luxury" = 0

In a real-life scenario, you might have a large dataset of customer ride preferences, with the "Ride Type" column containing values like "Economy," "Premium," and "Luxury."

1. Collect your data (e.g., customer ride preferences).
2. Apply LabelEncoder to transform these categorical values into numerical labels.
3. Use the encoded data in machine learning models, like Decision Trees, to predict future ride preferences based on customer attributes.

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What is One Hot Encoder?

When you're working with machine learning models, you’ll often encounter data that includes categories instead of numbers. One Hot Encoding is a method to convert these categorical values into a format that your model can work with. 

It’s important because most algorithms can’t process raw categorical data, and One Hot Encoding solves that problem by transforming the data into a numerical format.

How One Hot Encoding Works:

• Each category is converted into a new binary column.
• For each row, a 1 is placed in the column corresponding to the category, and 0 in the other columns.
• For example, "Red," "Blue," and "Green" will be split into three columns, where "Red" gets a 1 in the Red column and 0s in the others, and so on for the other colors.

When to Use?

• Nominal Data: This encoding is ideal for data with no natural order, like color names or product categories.
• Ordinal Data: One Hot Encoding is not typically used here, as ordinal data has a clear ranking (e.g., "Low," "Medium," "High"), where Label Encoding works better.

To make it easier to understand, let’s break down how One Hot Encoding works with a simple visual example. 

Suppose you have a dataset with the categories "Red," "Blue," and "Green". Using One Hot Encoding, each category is converted into a binary vector. Here’s how:

• Red becomes [1, 0, 0], which means "Red" is the first category, so the first column is 1, and the others are 0.
• Blue becomes [0, 1, 0], meaning it's represented in the second column.
• Green becomes [0, 0, 1], placed in the last column.

Let’s consider a real-life example where you have a dataset containing "Customer Preferred Payment Methods" such as "Credit Card," "Debit Card," and "PayPal." 

You want to use One Hot Encoding to convert these categorical payment methods into a format that a machine learning model can process.

Your data might look like this:

We apply One Hot Encoding to this dataset to convert the Payment Method column into binary vectors.

from sklearn.preprocessing import OneHotEncoder
import numpy as np

# Sample dataset with customer payment preferences
payment_methods = ['Credit Card', 'PayPal', 'Debit Card', 'Credit Card', 'PayPal']

# Reshaping data for OneHotEncoder (required for single feature columns)
payment_methods_reshaped = np.array(payment_methods).reshape(-1, 1)

# Creating the OneHotEncoder object
one_hot_encoder = OneHotEncoder()

# Fitting and transforming the data
encoded_payment_methods = one_hot_encoder.fit_transform(payment_methods_reshaped).toarray()

# Displaying the result
print("Encoded Payment Methods (One Hot Encoding):")
print(encoded_payment_methods)
print("Categories:", one_hot_encoder.categories_)

Output:

Encoded Payment Methods (One Hot Encoding):
[[1. 0. 0.]
[0. 0. 1.]
[0. 1. 0.]
[1. 0. 0.]
[0. 0. 1.]]
Categories: [array(['Credit Card', 'Debit Card', 'PayPal'], dtype=object)]

Explanation: 

• Data Setup: The payment_methods list contains customer payment preferences: "Credit Card," "PayPal," and "Debit Card."
• Reshaping Data: We reshape the list into a 2D array using np.array(payment_methods).reshape(-1, 1), which is required by OneHotEncoder.
• OneHotEncoder: We initialize the encoder with one_hot_encoder = OneHotEncoder().
• Fit and Transform: The .fit_transform() method applies the encoder and converts the categories into binary vectors.
• Output: The result is printed as encoded_payment_methods (the binary vectors) and one_hot_encoder.categories_ (the original categories).

Advantages & Disadvantages:

Now that you’re familiar with One Hot Encoding, remember to choose the right encoding method based on your data type. For larger datasets with many categories, consider alternatives like Feature Hashing or Binary Encoding.

To advance your skills, explore topics like Dimensionality Reduction or Target Encoding for handling complex data more efficiently. Keep experimenting, and your machine learning models will continue to improve.

Advance Your Machine Learning Skills with upGrad!

While Label Encoding is best for ordinal data, One Hot Encoding is ideal for nominal data, ensuring each category is treated independently. You might face challenges when dealing with high-cardinality features or large datasets, where One Hot Encoding can become memory-intensive.

To improve your model’s performance, focus on choosing the right encoding technique by understanding Label Encoder vs One Hot Encoder. For a deeper understanding of machine learning and data preprocessing, upGrad offers courses in data science and machine learning. 

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Reference:
https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html