What is the silhouette score in clustering?
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What is the silhouette score in clustering?
The silhouette score is a metric used to evaluate the quality of clustering. It measures how similar an object is to its own cluster compared to other clusters.
How is the silhouette score calculated?
The silhouette score is calculated using the formula: s = (b - a) / max(a, b), where 'a' is the average distance to points in the same cluster, and 'b' is the average distance to points in the nearest cluster.
What does a silhouette score of 1 mean?
A silhouette score of 1 indicates that the data point is very well clustered, meaning it is far from the nearest cluster and close to its own.
Can the silhouette score be negative?
Yes, a negative silhouette score indicates that the data point is likely assigned to the wrong cluster, as it is closer to points in another cluster.
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