Silhouette Score

The Silhouette Score is a metric used to evaluate the quality of clustering in machine learning. It measures how similar an object is to its own cluster compared to other clusters, providing insights into the appropriateness of the chosen clustering algorithm. The score ranges from -1 to +1, where a high value indicates that the data points are well-clustered, and a low or negative value suggests that the points may be incorrectly assigned. Common use cases include assessing clustering results in exploratory data analysis, optimizing clustering parameters, and comparing different clustering algorithms.