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Related Questions
- What are the common imputation methods used for handling missing values in a dataset?
- How does the proportion of missing values in the dataset influence the choice of imputation method?
- What are the differences between simple, regression-based, and k-Nearest Neighbors (KNN) imputation methods?
- Can you explain the concept of 'missing not at random' (MNAR) and its implications for imputation?
- How does the type of data (categorical vs. numerical) affect the choice of imputation method?
- What are the advantages and disadvantages of using mean/median imputation for handling missing values?
- How does the choice of imputation method impact the accuracy and reliability of downstream machine learning models?
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