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Related Questions
- What is the difference between under-imputation and over-imputation when dealing with missing values in a dataset?
- How does under-imputation impact the accuracy of a model when missing values are present in the training data?
- Can you explain the concept of under-imputation and how it affects model performance in the context of machine learning?
- What are the potential consequences of under-imputing missing values on the performance of a model in a regression task?
- How does under-imputation compare to other strategies for handling missing values, such as listwise deletion or mean imputation?
- In what scenarios is under-imputation a suitable strategy for handling missing values, and when might it be less effective?
- What are some common pitfalls to avoid when using under-imputation to handle missing values in a dataset?
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