Question

A data scientist is preparing a customer churn dataset. The 'age' feature is continuous and highly right-skewed, and the team wants to convert it into categorical bins so that each bin contains roughly the same number of customers, which will help a downstream tree ensemble handle the skew more evenly. Which binning strategy should they apply?

Accepted Answer

Quantile (equal-frequency) binning, which places cut points so each bin holds approximately the same number of records

Answer

Equal-width binning, which divides the age range into intervals of identical size

Answer

Standard scaling the age column so it has mean 0 and standard deviation 1

Answer

One-hot encoding the raw age values as-is into indicator columns