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Categorical variables are commonplace in many datasets, representing characteristics that don’t have inherent numerical values. However, most machine learning algorithms require numerical input. Therefore, it’s crucial to convert categorical variables into numerical representations.
In this tutorial, we’ll explore various techniques for performing this conversion, along with their advantages, disadvantages, and use cases.
- Description: Assigns a unique numerical value to each category.
- Use Case: Suitable for categorical variables with inherent ordinal relationships.
- Example: Converting categorical labels like “Low,” “Medium,” and “High” to 1, 2, and 3, respectively.
- Description: Creates binary columns for each category, with only one column active (1) per observation.
- Use Case: Suitable for nominal data where categories have no inherent order.
- Example: Converting categories like “Red,” “Green,” and “Blue” into binary columns (e.g., [1, 0, 0], [0, 1, 0], [0, 0, 1]).
- Description: Similar to one-hot encoding but leaves out one category as a reference.
- Use Case: Reduces redundancy and improves computational efficiency, especially with large datasets.
- Example: Encoding categories like “Small,” “Medium,” and “Large” into binary columns, leaving one as a reference.
- Description: Like dummy coding, but uses -1 instead of 0 for the reference category.
- Use Case: Useful for comparing each category against a reference group.
- Example: Encoding categories with -1 for the reference category and 1 for the others.
- Description: Represents categories with binary code, reducing dimensionality compared to one-hot encoding.
- Use Case: Efficient for large datasets with many categories.
- Example: Converting…