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Related Questions
- What are the potential consequences of biased sampling on the accuracy of machine learning models?
- How can biased sampling lead to overfitting or underfitting in machine learning models?
- Can you explain the concept of representativeness in the context of biased sampling and its impact on model generalizability?
- What are some common sources of biased sampling in machine learning datasets?
- How can data preprocessing techniques, such as data cleaning and feature engineering, affect the generalizability of machine learning models?
- What is the role of data augmentation in reducing the impact of biased sampling on machine learning models?
- Can you discuss the trade-offs between sampling methods, such as stratified sampling and random sampling, in terms of generalizability and computational efficiency?
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