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The ‘silhouette score’ is a metric used to evaluate the quality of clustering by measuring how similar each point is to its own cluster compared to other clusters.
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A high silhouette score suggests that clusters are compact and well-separated from each other.
The score ranges from -1 to 1, where a high value indicates that the point is well-matched to its own cluster and poorly matched to neighbouring clusters.
The calculation of the silhouette score involves two distances: the mean intra-cluster distance (a) and the mean nearest-cluster distance (b) for each sample. The silhouette score SSS for each point is computed as:
$$ S = \frac{b - a}{\max(a, b)} $$
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