What is the primary effect of applying One-Hot Encoding to a categorical feature with high cardinality?

Question

Data Science — Medium

What is the primary effect of applying One-Hot Encoding to a categorical feature with high cardinality?

Accepted Answer

One-Hot Encoding creates a new binary column for every unique category in a feature. When cardinality is high, this results in a sparse matrix with a massive number of columns, often leading to the 'curse of dimensionality' and increased memory usage. Option B describes label encoding, while C and D are incorrect as high-cardinality one-hot encoding typically increases model complexity rather than reducing it.