Which technique is used to convert categorical variables into binary columns?

Explanation

One-Hot Encoding creates a new binary column for each unique category in a feature, representing the presence or absence of that category. Label Encoding assigns an integer to each category, which may imply an incorrect ordinal relationship. Standardization and Mean Imputation are techniques for numerical data, not categorical encoding.

Key points

One-Hot Encoding creates binary indicator variables
Prevents models from assuming ordinal relationships
Increases dimensionality based on unique category count

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