Question

A data engineer is preparing a customer transactions dataset for a fraud-detection model. One column, 'merchant_id', contains over 50,000 unique values and is highly predictive. The team wants to convert this feature into a numeric representation without creating tens of thousands of sparse columns that would explode dimensionality and slow training. Which feature-engineering approach best addresses this requirement?

Accepted Answer

Use target (mean) encoding to replace each 'merchant_id' with the fraud rate associated with that merchant, computed on the training folds

Answer

Apply one-hot encoding to 'merchant_id' so each unique merchant becomes its own binary column

Answer

Drop the 'merchant_id' column entirely since high-cardinality features cannot be used in machine learning

Answer

Convert 'merchant_id' to a floating-point number by casting the raw identifier string to its numeric value