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Related Questions
- What are the scenarios where a higher level of label noise can be beneficial in an active learning setting, such as when there are few labeled examples and a large amount of noise can lead to a more accurate representation of the data distribution?
- How does the trade-off between label noise and the cost of acquiring new labels affect the choice of classification model in an active learning setting?
- What are the implications of using a classification model with a higher level of label noise when the dataset is imbalanced, and how can this be addressed?
- Can a higher level of label noise in a classification model lead to better generalization performance when the model is trained on a small subset of the data?
- How does the level of label noise in a classification model affect the selection of samples for active learning, and can a higher level of noise lead to a more efficient selection process?
- What are the scenarios where a higher level of label noise in a classification model can lead to a more robust model, such as when there is a large amount of missing data or outliers in the dataset?
- Can a higher level of label noise in a classification model be beneficial when the goal is to develop a model that can handle uncertainty and ambiguity in the data, such as in medical diagnosis or natural language processing?
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