What Categorical Encoding Meaning, Applications & Example
Process of converting categorical variables into numerical format.
What is Categorical Encoding?
Categorical Encoding is a process of converting categorical data (non-numeric data like “Red,” “Blue,” or “Green”) into a numerical format that machine learning models can use. Different encoding techniques are chosen based on the nature of the categories and the model requirements.
Types of Categorical Encoding
- One-Hot Encoding : Creates binary columns for each category. Useful when categories are nominal (no intrinsic order).
- Label Encoding: Assigns a unique integer to each category. Suitable for ordinal categories (with a meaningful order).
- Target Encoding: Replaces each category with the mean of the target variable. Commonly used in situations with high cardinality.
Applications of Categorical Encoding
- Predictive Modeling : Transforms categorical features, allowing models to process them alongside numeric features.
- Data Preprocessing: Facilitates the preparation of categorical data for a wide range of models, including tree-based and linear models.
- Customer Segmentation: Encodes demographic attributes (like location or profession) to identify customer patterns.
Example of Categorical Encoding
In loan approval prediction, attributes like “marital status” and “education level” are encoded, allowing the model to interpret and incorporate these factors in its predictions, leading to more accurate assessments.