Ordinal Encoding
Ordinal encoding is a data preprocessing technique used in machine learning and statistics to convert categorical variables with an inherent order (ordinal data) into numerical values. It assigns integer labels to categories based on their rank or hierarchy, preserving the ordinal relationship between them. This transformation enables algorithms that require numerical input, such as regression models or decision trees, to process ordered categorical data effectively.
Developers should use ordinal encoding when working with categorical features that have a clear ranking, such as education levels (e.g., high school, bachelor's, master's), customer satisfaction ratings (e.g., poor, fair, good, excellent), or size categories (e.g., small, medium, large). It is essential in machine learning pipelines to maintain the ordinal information while converting data into a format compatible with numerical models, improving model performance by capturing meaningful relationships without arbitrary encoding.