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Related Questions
- What are some common metrics used to evaluate the diversity of a dataset, such as entropy or mutual information?
- How can we measure the representation of underrepresented groups in a dataset, and what are some common biases to watch out for?
- What are some techniques for evaluating the representativeness of a dataset, such as stratified sampling or clustering?
- How can we use metrics like fairness and accuracy to evaluate the performance of a model on different subgroups in the data?
- What are some methods for visualizing and exploring the distribution of data to identify biases and imbalances?
- How can we use techniques like data augmentation or oversampling to increase the diversity and representation of a dataset?
- What are some challenges and limitations of evaluating and measuring diversity and representation in a dataset, and how can we address them?
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