Target encoding (mean encoding)
Target encoding, also known as mean encoding, is a technique used for encoding categorical features by replacing each category with the mean of the target variable (or another relevant aggregation function) for that category. This method is particularly useful for classification tasks when dealing with high-cardinality categorical features, where one-hot encoding would result in a significant increase in dimensionality.
In more detail, target encoding replaces categorical values with the mean (or other aggregation metric) of the target variable for each category. It leverages the relationship between the categorical feature and the target variable to encode the information.
When to use target encoding
When you have categorical features with many unique categories, using one-hot encoding might lead to a high-dimensional dataset. Target encoding can be an effective alternative in such cases.
If there’s a strong relationship between the...
