Objective 2.4 Transform Numerical and Categorical Data AIP-210 Practice Questions (Page 5)

Questions 21–24

1. 21foundation · easy

   Which encoding technique is most appropriate for a nominal categorical feature with a small number of categories (e.g., 3 categories) and no ordinal relationship?

   • AOne-hot encoding
   • BFrequency encoding
   • COrdinal encoding
   • DTarget encoding
   Reveal Solution Discussion Select an answer first
2. 22foundation · easy

   A dataset contains a categorical feature representing education level with values: 'High School', 'Bachelor's', 'Master's', and 'PhD'. Which encoding technique is most appropriate to preserve the ordinal nature of this feature?

   • ALabel encoding
   • BOrdinal encoding
   • COne-hot encoding
   • DBinary encoding
   Reveal Solution Discussion Select an answer first
4. 23foundation · easy

   Which encoding technique is specifically designed to handle high-cardinality categorical features by replacing each category with the mean of the target variable for that category?

   • AOrdinal encoding
   • BTarget encoding
   • COne-hot encoding
   • DLabel encoding
   Reveal Solution Discussion Select an answer first
5. 24foundation · easy

   What is the primary purpose of encoding categorical variables into numerical representations for machine learning models?

   • ATo make the data compatible with algorithms that require numerical input
   • BTo reduce the dimensionality of the dataset
   • CTo improve the accuracy of the model by adding more features
   • DTo handle missing values in categorical features
   Reveal Solution Discussion Select an answer first