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Related Questions
- Does uncertainty-based sampling struggle to capture non-linear relationships in high-dimensional input spaces?
- How does the performance of uncertainty-based sampling degrade as the dimensionality of the input space increases?
- Can uncertainty-based sampling effectively handle datasets with varying levels of class imbalance and high-dimensional input spaces?
- In datasets with large class imbalances, does uncertainty-based sampling risk over-sampling the majority class and under-sampling the minority class?
- How do different sampling strategies, such as subsampling or importance sampling, impact the performance of uncertainty-based sampling on high-dimensional datasets?
- What are the theoretical guarantees, if any, for uncertainty-based sampling to perform well on high-dimensional input spaces or large class imbalances?
- Can ensemble methods, such as bootstrapping or bagging, be used to improve the performance of uncertainty-based sampling on datasets with high-dimensional input spaces or large class imbalances?
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