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Related Questions
- What are some common pitfalls of using mean or median imputation for missing values in machine learning models?
- How can we use data visualization techniques to identify potential over-imputation of missing values?
- What are some strategies for selecting the most relevant features to impute missing values, rather than relying on random imputation?
- Can you explain the concept of 'imputation bias' and how it can affect the performance of machine learning models?
- How can we use model-based imputation methods, such as multiple imputation by chained equations (MICE), to reduce over-imputation of missing values?
- What are some techniques for evaluating the impact of missing data on model performance, and how can we use these techniques to detect over-imputation?
- Can you discuss the trade-offs between using simpler imputation methods, such as mean or median imputation, versus more complex methods, such as machine learning-based imputation, and how to choose the best approach for a given problem?
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