Câu 11: Databricks Certified Machine Learning Associate

An organization is developing a feature repository and is electing to one-hot encode all categorical feature variables. A data scientist suggests that the categorical feature variables should not be one-hot encoded within the feature repository. Which of the following explanations justifies this suggestion?

1. A. One-hot encoding is not supported by most machine learning libraries.
2. B. One-hot encoding is dependent on the target variable’s values which differ for each application.
3. C. One-hot encoding is computationally intensive and should only be performed on small samples of training sets for individual machine learning problems.
4. D. One-hot encoding is not a common strategy for representing categorical feature variables numerically.
5. E. One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms. — đáp án hiện tại