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Categorical Encoding: Methods to Transform Categorical Data
Published 14 May 2025
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Categorical encoding is a crucial step in preparing data for machine learning algorithms, which often require numerical input. Categorical variables, which take on a limited number of distinct values (such as color, gender, or type of product), need to be converted into a numerical format that can be understood by these algorithms. There are several methods for categorical encoding, each with its advantages, disadvantages, and suitable use cases. This blog will cover various encoding techniques, explaining their methods and potential applications.
Label encoding transforms each category into a unique integer value. Each unique category in a column is assigned an integer value starting from 0.
Suppose you have a color feature with the values "Red," "Green," and "Blue." Label encoding would convert these categorical values as follows:
Label encoding is suitable for ordinal categorical variables, where the order matters (e.g., "Low," "Medium," "High").
It may introduce unintended ordinal relationships for nominal variables, misleading the model into interpreting them as ordered.
One-hot encoding creates binary columns for each category in the variable. Each column is 1 if the observation belongs to that category and 0 otherwise.
For the color feature:
It is ideal for nominal categorical variables without inherent order, preventing any ordinal assumptions.
One-hot encoding can lead to a high-dimensional dataset if a categorical variable has many unique values, increasing computation time and memory usage.
Binary encoding is a compact representation of categories in binary format. It first converts categories into integers, and then those integers are converted to binary code.
For a color feature:
This results in only 2 columns as opposed to 3 in one-hot encoding.
Binary encoding is useful for high-cardinality categorical variables where one-hot encoding would create too many columns.
It may not be as interpretable as one-hot encoding and could create some degree of confusion regarding the binary representation.
Frequency encoding replaces each category with its frequency or count in the dataset.
For color where the occurrence is:
Frequency encoding is beneficial for categorical variables that have a high different ratio among their categories, and usually works well with tree-based models.
It can introduce noise when rare categories might receive too much significance.
Target encoding involves replacing each category with the mean of the target variable for that category. This method utilizes the relationship between the feature and the target variable.
For a feature indicating a product category and a target variable of sales:
Electronics, average sales = $500Clothing, average sales = $300Target encoding is particularly useful when the categorical variable has a strong correlation with the target variable.
It risks leakage when performed without proper cross-validation, potentially leading to overfitting.
Ordinal encoding is a method used to convert ordinal categories into integers while maintaining the order.
For an education level feature:
This method is best used with ordered categorical variables where the sequence matters.
Similar to label encoding, it is not appropriate for nominal variables, where the order is irrelevant.
Count encoding replaces each category with its frequency count rather than its frequency proportion. It establishes the base number for each category.
For the color feature:
Count encoding is useful when the frequency of the category itself is significant for predictions.
Similar to frequency encoding, it might confuse categories that appear equally.
Helmert encoding is used for ordinal categorical variables, comparing each category to the mean of the subsequent categories. It contrasts the category with the collective mean to derive
Example: If you have an ordinal variable representing education levels such as "High School," "Bachelor's," "Master's," and "PhD," Helmert encoding will compare each level against the average of the subsequent levels.
When to Use: This encoding is suitable for ordinal variables where you want to emphasize the difference between the current category and the averages of all higher categories.
Limitations: Helmert encoding may not provide significant benefits over simpler types of encoding for some datasets. It could also complicate the interpretability of the model if many levels exist.
There are numerous methods for encoding categorical variables, each with its strengths and weaknesses. Understanding these encoding techniques—such as label encoding, one-hot encoding, binary encoding, frequency encoding, target encoding, and more—can significantly influence the performance of machine learning models. The choice of encoding should depend on the nature of the categorical variable, the specific algorithm being used, and the overall analysis strategy. Effective feature engineering, including appropriate encoding of categorical variables, is vital for building robust and accurate predictive models.
Happy coding!