targetencoding

Target encoding, also known as mean encoding or likelihood encoding, is a feature engineering technique used in machine learning. It replaces categorical features with numerical values that represent the average of the target variable for each category. This process aims to capture the relationship between a categorical feature and the target variable in a numerical format that machine learning models can readily process.

The core idea is to calculate the mean of the target variable for each unique category within a feature. For example, if you have a categorical feature like "city" and a binary target variable (e.g., click or no click), target encoding would replace each city name with the proportion of "clicks" observed for that specific city. This transforms the categorical data into a continuous numerical feature.

While effective, target encoding can lead to overfitting, especially when dealing with rare categories or small datasets. To mitigate this, techniques like smoothing are often employed. Smoothing involves blending the category's mean with the overall mean of the target variable, with the degree of blending often depending on the frequency of the category. Cross-validation can also be used to prevent data leakage and ensure robust encoding. This method is particularly useful for high-cardinality categorical features where one-hot encoding might result in an unmanageably large number of dimensions.