What does ML.FEATURE_CROSS generate?

The option indicating that ML.FEATURE_CROSS generates a STRUCT feature with all combinations of crossed categorical features is accurate because the purpose of feature crossing is to construct interaction features that enable the model to capture the interactions between categorical variables. By crossing two or more categorical features, ML.FEATURE_CROSS creates a new feature for every possible combination of these categorical features, thus expanding the feature space.

This is particularly useful in scenarios where the relationship between the features may not be linear, allowing the machine learning model to learn more complex patterns. The resulting STRUCT feature consolidates these combinations, providing a valuable representation that can enhance model performance by incorporating interaction information.

The other options do not capture the function of ML.FEATURE_CROSS accurately. A mean of crossed features would typically summarize the information rather than generate combinations. Generating a linear regression model from multiple features does not align with the purpose of feature creation through crossing. Finally, a normalized feature set does not reflect the concept of feature crossing either, as normalization primarily pertains to scaling values rather than creating new features through combinations.