Machine Learning Resources

What is Silhouette Score?

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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