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Answer: Data Imputation
Correct Explanation: Data Imputation stands out as the pivotal technique for addressing missing values by filling them with estimated or calculated values derived from the existing data. Databricks MLlib offers several imputation methods, including mean or median imputation, to ensure the dataset's completeness. This approach safeguards valuable information, facilitating thorough analysis and modeling. Although Feature Scaling, Outlier Detection, and Feature Selection play significant roles in data preprocessing, they are not tailored to specifically tackle missing values. Imputation is indispensable for preparing a robust dataset ready for machine learning applications.
Author: LeetQuiz Editorial Team
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When your team encounters a dataset with missing values across several features, which Databricks MLlib-supported technique is most effective for handling these missing values during data preprocessing?
A
Feature Scaling
B
Outlier Detection
C
Data Imputation
D
Feature Selection
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