Categorical Encoding
Categorical encoding is a data preprocessing technique used in machine learning and statistics to convert categorical variables (non-numeric data like categories or labels) into numerical formats that algorithms can process. It involves transforming text-based or discrete categories into numeric representations, such as integers or binary vectors, to enable mathematical operations and model training. Common methods include label encoding, one-hot encoding, and target encoding, each suited for different types of categorical data and model requirements.
Developers should learn categorical encoding when working with machine learning models, as most algorithms (e.g., linear regression, neural networks) require numerical input and cannot directly handle categorical data like 'red', 'blue', or 'high', 'medium', 'low'. It is essential in use cases such as predictive modeling, data analysis, and feature engineering for datasets with categorical attributes, such as customer demographics in marketing or product categories in e-commerce, to improve model accuracy and performance.