Ordinal Encoding Machine Learning
Data preprocessing is critical for machine learning since it turns raw data into an algorithm-friendly format. Processing categorical data that reflects traits or features with a restricted number of values is an important part of data preprocessing. Since many machine learning methods require numerical input, categorical data cannot be used directly.
This can be solved with ordinal encoder. This encoding approach is specialized for categorical data with a defined hierarchy. This article will discuss ordinal encoding, its merits, drawbacks, and differences from other machine learning encoding methods.
What is Ordinal Encoding?
Ordinal encoder converts ordered category variables into numerical codes. The categories of ordinal variables follow a precise order, although the differences between categories are not necessarily equal or quantifiable. Ordinal encoding gives categories integer values to preserve order. The idea is to let machine learning algorithms comprehend and process data while keeping category ordinal links.
For ordered categorical data, ordinal encoding is appropriate, but not for nominal data. Another method for nominal data is one-hot encoding, which does not entail category order.
Importance of Ordinal Encoding in Machine Learning
Numerical input is typical for machine learning models. Ordinal encoder turns natural-order category data into numerical values. This transformation is crucial because linear regression, decision trees, and neural networks employ numeric input to compute patterns, predict, and execute other actions. Ordinal encoder preserves category relationships, allowing the model to use ordered data.
When dealing with features with few ordered categories, ordinal encoding is easy and computationally efficient. Converting categorical data to machine learning algorithm-compatible format improves model training.
When to Use Ordinal Encoding
When categorical data has a meaningful order, ordinal encoder is best. The ordering of categories can affect model predictions in some cases. Ordinal encoder is ideal for categories with a ranking or scale—the model needs to grasp the relationship between these levels.
When data is nominal, ordinal encoding should be avoided since it creates an artificial ranking in unordered categories, leading to incorrect assumptions and poor model performance. Ordinal encoder would be inappropriate for categorical data like colors or product types without a meaningful order.
Advantages of Ordinal Encoder
- Preserves the Order: Ordinal encoder maintains the intrinsic order in categorical data. It preserves category rankings, which is important for machine learning models that use them.
- Memory Efficient: Compared to one-hot encoding, ordinal encoding produces numerical values that use less memory. By adding a binary column for each category, one-hot encoding increases dataset dimensionality. Ordinal encoding addresses this difficulty by assigning a number value to each category, which is advantageous for high-cardinality datasets.
- No Increase in Dimensionality: Ordinal encoding does not add columns for each category like one-hot encoding. We avoid the computational expense of high-dimensional data and simplify the model.
- Compatibility with Tree-Based Models: Decision trees, random forests, and gradient boosting machines benefit from ordinal encoding. These models can better handle ordinal information when splitting feature values, boosting model accuracy.
- Compact Representation: Ordinal encoder is more compact than one-hot encoding, which may be useful for storing huge datasets or using limited computing resources.
Disadvantages of Ordinal Encoding
- Imposing a False Linear Relationship: False Linear Relationship One downside of ordinal encoder is that it may assume a linear relationship between encoded values. Even when categories are represented as numbers, machine learning algorithms may interpret the difference between successive categories as equal. Categories may differ in reality. The meaning of a category ranked “1” and “2” may differ from “2” and “3”. Linear regression and other techniques that assume values are linearly connected can obtain deceptive results.
- Model Misinterpretation: Some machine learning algorithms, especially those that regard features as continuous variables, may misread ordinal encoding numerical values. When there is no linear relationship between categories, this can cause model behavior errors. Linear regression algorithms may misinterpret numeric values as equally spaced intervals.
- Limited Applicability to Nominal Data: Nominal data has no inherent order, therefore ordinal encoding is unsuitable. Ordinal encoding nominal features would falsely shape the data, weakening model integrity. Ordinal encoding could complicate and worsen model performance for categorical data like country names and product IDs, which are unordered.
- Difficulty in Handling Unseen Categories: In machine learning models, handling unseen categories (categories that are not in the training dataset but in the test set) can be difficult. Ordinal encoding calculates integer values for categories by order, however the model may not know how to handle a new category during testing or deployment. Such situations require solutions like considering new categories as a separate “other” category or adjusting the encoding during training.
- Limited Expressiveness: Ordinal encoding merely captures the ordinal aspect of the data (i.e., the order of categories), not the actual distance between categories. If category spacing is crucial to the model’s comprehension, this can cause information loss.
How Ordinal Encoding Works?
Key steps are needed to implement ordinal encoding. Identify and sort feature categories in natural order. Then, each category’s location in this ordered sequence determines its numerical value. After numerically encoding the categories, the feature can be substituted with the dataset’s integer values for machine learning algorithms that require numeric input.
You should know that encoded integers don’t carry any additional data meaning. The algorithm’s interpretation of numeric relationships determines the model’s capacity to learn from integers, which are symbolic representations of categories.
Challenges and Considerations
Ordinal encoder is simple and effective, but several factors must be considered. Major considerations include machine learning algorithm type. Different algorithms are more sensitive to encoded values. Since tree-based algorithms split data by feature values, they preserve ordinal relationships and perform well for ordinal encoding. However, ordinal encoding’s assumptions may make linear regression and distance-based methods like k-nearest neighbors difficult.
Encoding meaningfully ordered data is another consideration. Ordinal encoding should only be used when categories are ranked logically. Without knowing category relationships, indiscriminately applying ordinal encoding can lead to poor model performance and erroneous data interpretations.
Monitoring ordinal encoding’s effect on model performance is crucial. Ordinal encoding may not improve performance depending on the data and technique. Therefore, it’s important to try multiple encoding methods and assess the results to choose the optimal one for the situation.
Alternatives to Ordinal Encoding
Alternate encoding methods may be better for some categorical data than ordinal encoding:
- One-Hot Encoding: For nominal categorical data, one-hot encoding is popular. Create a binary column for each category with a vector of zeros and ones for each observation. Nominal data without order works well with this strategy.
- Label Encoding: Label encoding is like ordinal encoding but requires no category order. Label encoding gives categories unique integers without ranks. If no intrinsic ordering exists, it can be utilized on ordinal data as well as nominal data.
- Binary Encoding: One-hot and label encoding are combined in binary encoding. Compared to one-hot encoding, it encodes categories as binary digits, reducing dimensionality while keeping some category information.
Conclusion
Machine learning categorical data with inherent order or ranking can be handled easily and efficiently with ordinal encoder. Models can process categorical features while keeping category associations. It also has drawbacks, such as enforcing a false linear link between categories. The encoded data and machine learning model should be carefully considered. To maximize machine learning performance, ordinal encoding should be utilized carefully and in the right settings.