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One-Hot Encoding in Machine Learning with Python

One-Hot Encoding in Machine Learning with Python Cover Image

Feature engineering is an essential part of machine learning and deep learning and one-hot encoding is one of the most important ways to transform your data’s features. This guide will teach you all you need about one hot encoding in machine learning using Python. You’ll learn grasp not only the “what” and “why”, but also gain practical expertise in implementing this technique using Python.

Why dedicate an entire guide to one-hot encoding? The process is both fundamental and frequently encountered. Because feature engineering is often the linchpin of a successful model, I’m excited to share my knowledge and insights on the details of one-hot encoding, including its advantages, disadvantages, and implementations across the Python ecosystem.

By the end of this guide, you’ll have learned the following:

  • What one-hot encoding is and how to use it in machine learning
  • The advantages and disadvantages of one-hot encoding for machine learning
  • How to perform one-hot encoding in popular Python libraries including Sklearn and Pandas
  • How to work with large categorical variables in one-hot encoding
  • What some alternatives are to one-hot encoding in machine learning

Ready to get started? Let’s dive right in!

What is One Encoding in Machine Learning?

One Hot Encoding is a machine learning feature engineering technique that is used to transform categorical data into a numerical format, which makes it accessible for training machine learning models. In particular, one hot encoding represents each category as a binary vector where only one element is “hot” (set to 1), while the others remain “cold” (or, set to 0).

Personally, I find this is best explained with an example. Let’s take a look at the image below:

Understanding One Hot Encoding for Dealing with Categorical Data in Machine Learning
Understanding One Hot Encoding for Dealing with Categorical Data in Machine Learning

We can see in the image that a single column is turned into three columns. The number of columns that the values are encoded into depends on the number of unique values in that column. This is a challenge that we’ll continue exploring later on in the tutorial, as one-hot encoding can lead to very sparse matrices.

Let’s dive into a bit of terminology before we continue:

By one-hot encoding a column, we convert it into a number of binary vectors, which are represented with hot values (1s) and cold values (0s).

Now that you have a good understanding of the mechanics of one-hot encoding, let’s dive into why you may want to use it for machine learning feature engineering.

Why Use One Hot Encoding in Machine Learning?

Using one hot encoding is useful when you’re working with a feature (that is, a column) where the data has no relationship to one another. Because machine learning algorithms can only work with numbers, we need to be mindful of how we encode our values. By assigning numbers for categorical data, we apply an attribute of significance to our data.

Going back to our earlier example, we could have used the following encoding:

  • πŸ₯— = 1
  • πŸ” = 2
  • πŸ– = 3

However, by doing this, we would be implying that the meat is 2 units higher than salad. Similarly, we would be saying that the burger is 1 unit higher than the salad.

However, this encoding doesn’t make much sense. By using one-hot encoding, we ensure that the input data does not have any ranking for the categorical data. This will help improve our predictions and lead to better overall performance.

One hot encoding allows you to make sure that your data can be rescaled easily. This is beneficial when working with many different categories, allowing your data to be more expressive.

Let’s now take a look at the advantages of using one-hot encoding in machine learning.

Advantages of One Hot Encoding in Machine Learning

In this section, you’ll learn about some of the advantages of using one hot encoding in machine learning. Let’s take a look at why it matters and how it helps your model be strong:

Preservation of categorical information: One hot encoding retains the categorical nature of data by converting it to binary vectors. Doing this allows you to ensure that data isn’t lost in the encoding process.
Elimination of numerical assumptions: One hot encoding allows your categorical data to be encoded into a numerical format without building assumptions of magnitude into your data (such as ordinal encoding would).
Enhanced model performance: One hot encoding allows your model to learn from categorical data, allowing the data to be easily differentiated.
Reduced risk of bias: Since machine learning models need to work with numbers, your categorical data needs to be encoded. Rather than using ordinal (e.g., 1, 2, 3, etc.) encoding, one hot encoding eliminates any artificial ordinal relationships between categories, removing the risk of bias.
Interpretability and feature importance: One hot encoding allows you to easily understand which categories are more influential in your model’s decision-making abilities.

But, hey, it’s not all sunshine and roses! In the following section, we’ll explore the disadvantages of using one hot encoding for machine learning.

Disadvantages of One Hot Encoding in Machine Learning

While One Hot Encoding provides many benefits, it also comes with some disadvantages. In this section, you’ll learn about some of the issues that may arise from using one hot encoding for transforming categorical data:

High dimensionality: One hot encoding has the potential to significantly increase the dimensionality of a dataset. This is because one column is added for each unique value in a given column. In our earlier example, we had three unique values, which resulted in three columns. Imagine working with a feature that had hundreds of unique values!
Increased storage requirements: This is a byproduct of higher dimensionality – by storing the binary vectors that are generated when we one hot encode data, our storage requirements increase as well.
Multicollinearity issues: One hot encoding can also result in multicollinearity, which is an event that occurs when two or more one hot encoded features are highly correlated. This can result in problems such as linear regression where the stability of coefficient estimates is affected.
Handling rate categories: Categories that are rare or infrequent in a categorical column can result in sparse one-hot encoded binary vectors that are mostly zeros. This can lead to an issue where machine learning algorithms
Increased computation time: Because the data will be of a higher dimension, certain machine learning algorithms will require longer to train and to make predictions. This can be an issue, especially when working with real-time predictions.
Interpretability challenges: When working with a large number of one-hot encoded features, the specific importance attributed to each feature (let alone, the original) can be difficult to interpret. This can be an issue, in particular when communicating model decisions to project stakeholders.

Now that you have a solid understanding of the advantages and disadvantages of one hot encoding categorical data for machine learning, let’s now dive into how you can one-hot encode your data in Python, beginning with scikit-learn!

How to Perform One Hot Encoding in Python with Sklearn

Sklearn comes with a one-hot encoding tool built-in: the OneHotEncoder class. The OneHotEncoder class takes an array of data and can be used to one-hot encode the data. As with many other elements in sklearn, there are a ton of different options available, though they all follow a familiar syntax.

If you want to learn more about One Hot Encoding values in Scikit Learn, check out my in-depth guide on using the sklearn library for encoding categorical data. The guide will take you through a start-to-finish walkthrough of using the popular machine-learning library for one-hot encoding.

Let’s take a look at the different parameters the class takes:

# Understanding the OneHotEncoder Class in Sklearn
from sklearn.preprocessing import OneHotEncoder

OneHotEncoder(
    categories='auto',  # Categories per feature
    drop=None, # Whether to drop one of the features
    sparse=True, # Will return sparse matrix if set True
    dtype=<class 'numpy.float64'>, # Desired data type of the output
    handle_unknown='error' # Whether to raise an error 
)

We can see that we get quite a bit of flexibility in how to use one-hot encoding in scikit-learn. Let’s see how we can load a sample dataset and begin our process of one-hot encoding.

from sklearn.preprocessing import OneHotEncoder
from sklearn.datasets import fetch_openml
import pandas as pd

X, y = fetch_openml(
    "titanic", 
    version=1, 
    as_frame=True, 
    return_X_y=True, 
    parser='auto')

X = X[['sex', 'age', 'embarked']]

print(X.head())

# Returns:
#       sex      age embarked
# 0  female  29.0000        S
# 1    male   0.9167        S
# 2  female   2.0000        S
# 3    male  30.0000        S
# 4  female  25.0000        S

In the code block above, we first loaded a dataset (the popular titanic dataset) and filtered it down to a limited number of columns to make it easier to manage. Let’s now create a one-hot encoder and encode our embarked feature.

# One-hot Encoding Data in sklearn
ohe = OneHotEncoder()
encoded = ohe.fit_transform(X[['embarked']]).toarray()
print(encoded)

# Returns:
# [[0. 0. 1. 0.]
#  [0. 0. 1. 0.]
#  [0. 0. 1. 0.]
#  ...
#  [1. 0. 0. 0.]
#  [1. 0. 0. 0.]
#  [0. 0. 1. 0.]]

We can see that our data are now one-hot encoded. Let’s see how we can add this back into our original DataFrame. We’ll do this by concatenating this new array back into our original feature matrix, X.

# Merging One Hot Encoded Data Back into the Original Dataset
ohe_df = pd.DataFrame(encoded, columns=ohe.get_feature_names_out())
X = pd.concat([X, ohe_df], axis=1)
print(X.head())

# Returns:
#       sex      age embarked  embarked_C  embarked_Q  embarked_S  embarked_nan
# 0  female  29.0000        S         0.0         0.0         1.0           0.0
# 1    male   0.9167        S         0.0         0.0         1.0           0.0
# 2  female   2.0000        S         0.0         0.0         1.0           0.0
# 3    male  30.0000        S         0.0         0.0         1.0           0.0
# 4  female  25.0000        S         0.0         0.0         1.0           0.0

In the code block above, we first created a new DataFrame, ohe_df, which contains our one-hot encoded values. We also assigned column labels by getting them using the .get_feature_names_out() method. We then used the pd.concat() function to merge these back together.

Let’s now dive into another popular library for working with tabular data: Pandas.

How to Perform One Hot Encoding in Python with Pandas

In addition to using Scikit-learn, we can also one hot encode our categorical data in Python using the Pandas library. Pandas offers the pd.get_dummies() to transfer categorical variables into one-hot encoded features. In this section, we’ll explore how this works by using a step-by-step example.

If you want to learn more about One Hot Encoding values in Pandas, check out my in-depth guide on using the Pandas library for encoding categorical data. The guide will take you through a start-to-finish walkthrough of using the popular data analysis library for one-hot encoding.

Let’s start by loading a sample DataFrame using only a single column:

# Loading a Sample Pandas DataFrame
import pandas as pd
df = pd.DataFrame(data=['Orange', 'Yellow', 'Blue', 'Blue', 'Yellow', 'Orange'], columns=['Color'])
print(df.head())

# Returns:
#     Color
# 0  Orange
# 1  Yellow
# 2    Blue
# 3    Blue
# 4  Yellow

We can see that our column has three unique values (which, we could verify using the Pandas unique method). This means that we should expect three columns to be created when we encode them. Let’s take a look at how we can implement the pd.get_dummies() function to one-hot encode out Pandas DataFrame.

# How to One-hot Encode Data in Pandas
encoded_df = pd.get_dummies(data=df, columns=['Color'], prefix='ohe')
print(encoded_df)

# Returns:
#    ohe_Blue  ohe_Orange  ohe_Yellow
# 0     False        True       False
# 1     False       False        True
# 2      True       False       False
# 3      True       False       False
# 4     False       False        True
# 5     False        True       False

In the code block above, we created a new Pandas DataFrame by passing in a number of different parameters:

  • data= takes in a Pandas DataFrame (or Series) that holds out data
  • columns= takes in a list of columns that we want to encode. In this case, we pass in our only column
  • prefix= is an optional parameter that allows us to set a prefix for the one hot encoded columns

Now, we have created a separate DataFrame, but it’s likely that we want to merge this back into our original DataFrame. In order to do this, we can use the Pandas concat function to merge the two DataFrames.

# Merge the One Hot Encoded Data into the Original Data
df = pd.concat([df, encoded_df], axis=1)
print(df)

# Returns:
#     Color  ohe_Blue  ohe_Orange  ohe_Yellow
# 0  Orange     False        True       False
# 1  Yellow     False       False        True
# 2    Blue      True       False       False
# 3    Blue      True       False       False
# 4  Yellow     False       False        True
# 5  Orange     False        True       False

When working in Pandas, it can be convenient to stay within the library for simpler transformations. It’s powerful that Pandas offers valuable tools for enhancing your data preprocessing pipeline. In the next section, we’ll dive into dealing with large categorical variables, addressing potential challenges when working with extensive categories.

Dealing with Large Categorical Variables in One Hot Encoding

A common challenge when working with one-hot encoding that dealing with categorical variables that have a large number of unique categories. As you learned in the earlier section on disadvantages of one-hot encoding, when dealing with large numbers of unique categories, one-hot encoding can lead to increased dimensionality and computational complexity.

In this section, we’ll explore some of the ways in which you can more easily work with large categorical variables. Let’s dive in!

  • Feature selection: by considering whether all categories are essential for your analysis, you can perform feature selection to reduce dimensionality, prior to applying one-hot encoding.
  • Grouping rare categories: When there are many rare categories, it can sometimes be helpful to group them into a single category (such as “Other” or “Rare”).
  • Hashing trick: instead of creating one-hot encoded columsn for each category, you can use a hashing trick to map categories to a fixed number of columns. This functionality is made possible in Scikit-Learn using the FeatureHasher class.
  • Domain knowledge: By leveraging domain knowledge, you can make informed decisions on how to handle large categorical variables. This can allow you to group data in a more informed manner.

In summary, handling large categorical variables in one-hot encoding requires a thoughtful approach. You can find effective ways to balance dimensionality reduction and preserving the essential information. As you can see from the list above, this handling large categorical variables is both an art and a science.

In the final section below, we’ll explore some alternatives to one-hot encoding for machine learning.

Alternatives to One Hot Encoding for Machine Learning

With everything you have learned about one-hot encoding in this guide, you might be thinking, “Great! Let’s go! Why use anything else?”. There are lots of other opportunities to encode your data that may be more appropriate for your specific problem.

Depending on the dataset and the nature of your features, alternative encoding techniques may offer better solutions. Let’s explore some of the alternatives and when you may want to use them.

Encoding TechniqueApproachUse Case
Label EncodingAssigns a unique integer to each categorySuitable for ordinal data, where the categories have a meaningful order
Ordinal EncodingSimilar to label encoding, but allows you to specify the order of categories explicitlyIdeal for ordinal categories, such as “low”, “medium”, and “high” where the order matters
Target Encoding (Mean Encoding)Replaces each category with the mean of the target variable for each categoryBeneficial when you want to capture the relationship between a categorical variable and the target variable, though it can lead to data leakage
Frequency EncodingReplaces each category with its frequency in the datasetCan be useful for high-cardinality categorical variables as it captures the importance of each category based on the prevalence
Leave-one-out EncodingReplace each category with the mean of the target variable for each category, excluding the current observationUseful for classification tasks and avoids data leakage while capturing the relationship between the categorical variable and the target
Alternatives to One-Hot Encoding for Categorical Variables

Choosing the encoding method really depends on the nature of your categorical data. This means that you might choose one approach for one feature and another method for another feature. It’s important to consider the strengths and limitations of each technique and experiment with different approaches.

Conclusion

In this comprehensive guide on One Hot Encoding in machine learning, you learned the essential techniques for handling categorical data. Because categorical data is so pervasive in real-world datasets, understanding how to encode them is crucial for building accurate and robust machine-learning models.

First, you learned what one-hot encoding is and how it converts categorical data into a numerical format while preserving crucial information. We then explored some of the advantages and disadvantages of one-hot encoding, allowing you to get a full sense of the encoding technique.

From there, we explored how to implement one-hot encoding in both Scikit-Learn and Pandas, giving you a full sense of the technique’s implementation. I also included links to resources that point to comprehensive guides on these implementations in Python.

Finally, we explored how to work with large categorical variables using one-hot encoding, as well as some alternatives to the popular encoding technique.

To learn more about the Pandas get_dummies function, check out the official documentation.

Nik Piepenbreier

Nik is the author of datagy.io and has over a decade of experience working with data analytics, data science, and Python. He specializes in teaching developers how to use Python for data science using hands-on tutorials.View Author posts

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