Silhouette Score compares the distance of observations to the centroids of the clusters they are assigned to against that to the centroids of other clusters in an algorithm like K-Means. It ranges between -1 and 1, where values close to -1 indicate that observations might have been assigned to the wrong cluster, where values close to 1 imply observations are close to the centroids of their own cluster but far from centroids of other clusters, which is indicative of compact clustering. Values closer to 0 indicate possible overlap between clusters, meaning it is ambiguous which cluster an observation should belong to. The average Silhouette score across all observations is reported to arrive at a global measure
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