Question

A data scientist is preparing a customer churn dataset that contains a high-cardinality categorical feature, 'zip_code' (over 30,000 unique values). They decide to use target (mean) encoding, replacing each zip code with the average churn rate observed for that zip code. To avoid inflating validation performance, which approach should they use when computing the encoding?

Accepted Answer

Compute the mean churn rate per zip code using only the training split, then map those values onto both the training and test splits (using a global mean for unseen categories).

Answer

Compute the mean churn rate per zip code using the full dataset before splitting, so every row gets a stable, consistent encoding.

Answer

Compute the mean churn rate per zip code separately within the training split and within the test split, so each split reflects its own churn behavior.

Answer

One-hot encode the zip code feature instead, since target encoding always introduces leakage regardless of how it is computed.