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Related Questions
- What are the key differences between stratified sampling and oversampling in the context of training data?
- Can you explain how oversampling can help mitigate class imbalance in datasets and improve model performance?
- How does stratified sampling ensure that the training data is representative of the population, and what are its benefits?
- What are some common techniques for implementing stratified sampling in machine learning, and what are their advantages?
- Can you provide examples of scenarios where oversampling is more effective than undersampling in improving model performance?
- How does the choice of sampling technique (stratified or oversampling) impact the model's ability to generalize to new, unseen data?
- What are some potential pitfalls or challenges associated with using stratified sampling and oversampling, and how can they be addressed?
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