Ordinal Encoding vs Label Encoding

Developers should use ordinal encoding when working with categorical features that have a clear ranking, such as education levels (e meets developers should use label encoding when working with machine learning models like decision trees, random forests, or gradient boosting that can handle integer-encoded categorical features efficiently, especially for nominal data with no inherent order. Here's our take.

Ordinal Encoding

Nice Pick

Developers should use ordinal encoding when working with categorical features that have a clear ranking, such as education levels (e

Pros

• g
• Related to: categorical-encoding, feature-engineering

Cons

• Specific tradeoffs depend on your use case

Label Encoding

Developers should use Label Encoding when working with machine learning models like decision trees, random forests, or gradient boosting that can handle integer-encoded categorical features efficiently, especially for nominal data with no inherent order

Pros

• It is particularly useful in scenarios with high-cardinality categorical variables where one-hot encoding would create too many sparse features, helping to reduce dimensionality and computational cost
• Related to: one-hot-encoding, feature-engineering

Cons

• Specific tradeoffs depend on your use case

The Verdict

Use Ordinal Encoding if: You want g and can live with specific tradeoffs depend on your use case.

Use Label Encoding if: You prioritize it is particularly useful in scenarios with high-cardinality categorical variables where one-hot encoding would create too many sparse features, helping to reduce dimensionality and computational cost over what Ordinal Encoding offers.

The Bottom Line

Ordinal Encoding wins

Developers should use ordinal encoding when working with categorical features that have a clear ranking, such as education levels (e