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Related Questions
- What is the Silhouette score, and what does it measure in the context of clustering algorithms?
- How does the Silhouette score take into account the separation and cohesion of clusters in evaluating clustering algorithms?
- What are the main assumptions made by the Silhouette score when evaluating the quality of clusters?
- Can the Silhouette score be used with any type of data, or are there specific data characteristics that it assumes?
- How does the Silhouette score handle outliers and noise in the data, and what are the implications for its assumptions?
- What is the relationship between the Silhouette score and other clustering evaluation metrics, such as the Calinski-Harabasz index?
- Are there any limitations or potential biases in the Silhouette score that users should be aware of when using it to evaluate clustering algorithms?
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