Binary Encoding vs One-Hot Encoding: Choosing the Right Approach for Your ML Models
When working with categorical data in machine learning, selecting the right encoding technique is critical for model performance. Two popular methods, Binary Encoding and One-Hot Encoding (OHE), are often used to convert categorical variables into numerical values that models can interpret. While both methods are effective, they have distinct advantages and trade-offs depending on your data and model. This blog dives into — “how to choose between Binary Encoding and OHE”, with a focus on comparison rather than explaining the mechanics of each method.
Quick Recap: What Are Binary Encoding and One-Hot Encoding?
Before we compare, let’s briefly touch on what these methods do.
- One-Hot Encoding (OHE)** converts each category into a new binary column where only one category is “hot” (1) and all others are “cold” (0).
- Binary Encoding first assigns each category a unique integer, which is then converted into its binary form. This reduces dimensionality compared to OHE.
Now that we know what they are, let’s explore when you should use each.
Dimensionality: Compactness vs Simplicity
One of the most obvious differences between Binary Encoding and OHE is the number of new features they create.
- One-Hot Encoding: For every unique category, a new binary feature is created. If you have 50 categories, you get 50 new columns. This can become unmanageable, especially with high-cardinality features (e.g., zip codes or product IDs).
When to Prefer OHE — If you have a small to medium number of categories, OHE is usually manageable and provides a clear, interpretable structure where each feature directly corresponds to a category. - Binary Encoding : Here, the number of new features grows logarithmically with the number of categories, reducing dimensionality significantly. For instance, if you have 50 categories, Binary Encoding will only generate 6 columns (since log₂(50) ≈ 6).
When to Prefer Binary Encoding — For high-cardinality categorical variables, Binary Encoding is far more efficient, reducing both memory consumption and computational load. It’s especially useful when scaling models across large datasets.
Distance Preservation: Uniform vs. Non-Uniform Representation ~(Nominal vs. Ordinal)
The distance between categories plays an important role, especially in algorithms that rely on feature similarity, such as K-nearest neighbors (KNN) or Support Vector Machines (SVM).
- OHE : All categories are treated as equally distinct from one another. The Hamming distance (the number of differing bits) between any two categories is always the same. This works well when categories are nominal, meaning they have no natural order or inherent relationship.
When OHE shines — is the better choice when your data is purely nominal. Examples include categorical variables like color (red, green, blue), where there’s no natural order or ranking among the categories. Since OHE treats all categories as distinct, it works perfectly in these cases. - Binary Encoding: This method introduces non-uniform distances between categories due to the binary representation. Two categories may differ by only one bit, making them appear closer than they really are. This can lead to misleading relationships between categories, especially when they are nominal and should be treated as equally distant. When Binary Encoding works — dimensionality reduction is a priority and the relationships between categories aren’t essential, Binary Encoding is a good choice. It also performs adequately with ordinal data where categories have a natural order (e.g., size: small, medium, large) but where the exact distances between them don’t need to be preserved.
Model Compatibility: How Different Models React
Not all models react the same way to categorical encodings. The type of model you’re using can have a significant influence on whether Binary Encoding or OHE is the right choice.
- Tree-based Models (Random Forest, XGBoost): These models are relatively insensitive to the choice of encoding. Whether you use Binary Encoding or OHE, tree-based algorithms split the data based on feature values rather than relying on distance metrics. Binary Encoding is often preferred here because it keeps the feature space smaller, which helps with model efficiency.
- Distance-based Models (KNN, SVM): Models that use distance metrics can be sensitive to encoding. In such cases, OHE performs better because it creates uniform distances between categories. Binary Encoding could introduce misleading distances, skewing the model’s perception of category similarity.
- Linear Models (Logistic Regression, Linear Regression): Linear models assume linear relationships between features and the target variable. Since Binary Encoding introduces non-uniform distances, it could distort these relationships, leading to poor performance. OHE is generally the safer bet with linear models.
- Deep Learning Models: Neural networks and deep learning models tend to be more robust to encoding issues. They can learn complex, non-linear representations, and with enough data, they often compensate for the downsides of Binary Encoding. Deep learning models often benefit from embedding layers, which learn dense, meaningful representations of categorical variables regardless of encoding.
Scalability and Efficiency: Computational Considerations
A key consideration when choosing between Binary Encoding and OHE is how they affect computational efficiency.
- One-Hot Encoding: OHE can quickly become computationally expensive when the number of categories increases. Every new category adds another column, increasing the data’s dimensionality, which can slow down training and increase memory usage.
When to Use OHE — For datasets where the number of categories is relatively small, and computational resources are not a bottleneck. - Binary Encoding: Since Binary Encoding generates fewer features, it is far more computationally efficient for large datasets. The reduced dimensionality allows models to train faster and use less memory.
When to Use Binary Encoding — When you are working with **high-cardinality categorical data** and need to ensure that the training time and memory usage remain manageable. Examples include datasets with thousands of unique product IDs, customer IDs, or locations.
Practical Guide to Choosing Between Binary Encoding and OHE
When deciding between Binary Encoding and OHE, consider the following factors:
1. Dimensionality: Use Binary Encoding for high-cardinality features; OHE for lower cardinality.
2. Model Compatibility: Distance-sensitive models (like KNN or SVM) benefit from OHE, while tree-based models and deep learning models can often handle either.
3. Scalability: If you’re working with large datasets, Binary Encoding’s compactness can greatly improve efficiency.
4. Data Type: OHE for nominal data, Binary Encoding for ordinal data where reducing dimensionality is important.
Real-World Example: In industries like e-commerce or advertising, where datasets often include features with thousands of unique identifiers (e.g., products, users), Binary Encoding can reduce training time and memory usage significantly without sacrificing too much accuracy.
In conclusion, while OHE provides simplicity and consistency, Binary Encoding offers efficiency and scalability. The key is to align your encoding choice with your dataset and model requirements, ensuring you balance interpretability, performance, and computational efficiency.
Now that you have a clear understanding of when to use Binary Encoding or OHE, try implementing both methods in your own projects and see how they affect model performance!
