Which encoding method is used for nominal data with no natural order, often creating binary indicators for each category?

Which encoding method is used for nominal data with no natural order, often creating binary indicators for each category?

• A. One-Hot Encoding
• B. Ordinal Data Encoding
• C. Imputation
• D. Data Scaling

Answer: (A)

One-hot encoding is used for nominal data with no natural order because it turns each category into its own binary feature. For a variable with categories like red, green, and blue, you get three columns where a row has a 1 in the column for its category and 0s in the others. This approach preserves the distinct identity of each category without implying any ranking, which is important since nominal data lack inherent order. It also makes it straightforward for machine learning models to treat each category as a separate feature. Be mindful that many categories can explode the number of features, in which case alternatives like target encoding or hashing tricks might be considered. Imputation is about filling missing values, and data scaling adjusts numeric ranges, neither of which encodes nominal categories.

One-hot encoding is used for nominal data with no natural order because it turns each category into its own binary feature. For a variable with categories like red, green, and blue, you get three columns where a row has a 1 in the column for its category and 0s in the others. This approach preserves the distinct identity of each category without implying any ranking, which is important since nominal data lack inherent order. It also makes it straightforward for machine learning models to treat each category as a separate feature. Be mindful that many categories can explode the number of features, in which case alternatives like target encoding or hashing tricks might be considered. Imputation is about filling missing values, and data scaling adjusts numeric ranges, neither of which encodes nominal categories.