Target Encoding
Target encoding is a feature engineering technique used in machine learning to encode categorical variables by replacing each category with the mean (or other statistic) of the target variable for that category. It leverages the relationship between categorical features and the target to create informative numerical representations, often improving model performance on high-cardinality categorical data. This method is particularly common in supervised learning tasks like classification and regression.
Developers should learn target encoding when working with categorical data that has many unique values (high cardinality), as traditional one-hot encoding can lead to sparse, high-dimensional datasets. It is especially useful in competitions like Kaggle or in production models for tabular data, such as predicting customer churn or sales, where it can capture meaningful patterns without excessive dimensionality. However, it requires careful implementation to avoid data leakage, often using techniques like cross-validation or adding noise.