Frequency Encoding
Frequency encoding is a feature engineering technique in machine learning and data science that converts categorical variables into numerical values based on the frequency or count of each category in the dataset. It replaces categories with their occurrence counts or relative frequencies, helping to capture the distribution of categories. This method is particularly useful for handling high-cardinality categorical features where one-hot encoding might be inefficient.
Developers should learn frequency encoding when working with categorical data in machine learning models, especially for tree-based algorithms like decision trees or gradient boosting, as it can improve model performance by reducing dimensionality compared to one-hot encoding. It's ideal for datasets with many unique categories, such as zip codes or product IDs, where frequency might correlate with the target variable. However, it should be used cautiously to avoid data leakage, ensuring frequencies are computed on training data only.