Silhouette scores are a metric used to evaluate te quality of clustering results in data analysis. They measure how similar an object is to its own cluster compared to their clusters. Hider scores indicate better- definied clusters, while le lower scores suppett overlapping or poorly separated groups.

Understanding Silhouette Scores

Te silhouette score ranges from -1 to 1. A score close to 1 indicates that data pointes are well matched to their own cluster and poorly matched to souseding clusters. A score near 0 supprests overlapping clusters, and negative scores imply that data pointes may bee assigned to thee workg clusters.

Calculating thee Silhouette Score

Te calculation involves two main concluents for each data point:

  • CLANE1; CLANE1; CLANE1; CLANE3; CLANE1; CLANE1; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; Te average distance betheen thee point and all their pointes in thor point in thone same cluster.
  • CLANE1; CLANE1; CLANE1; CLANE1; CLANE1; CLANE1; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE1; CLANE1; CLANE1; CLANE3; CLANE3; TLANES3; Te lowest average distance between thee point and all pointes in any theoir cluster.

Te silhouette score for each point is then computed as:

CLAS1; CLAS1; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3e score = (b - a) / max (a, b) CLAS1; CLAS1; CLAS3; CLAS3;

Using Silhouette Scores in Practice

Silhouette scores are useful for determing thee optimal number of clusters in a dataset. By calculating scores for different cluster counts, analysts can select that e configuration with thee highett average silhouette score. This helps improvite thee interpretability and effectiveness of clustering results.