What does the silhouette score measure?
mediumAnswer
- For each point, silhouette = (b - a) / max(a, b), where a is the mean distance to points in its own cluster and b is the mean distance to points in the nearest other cluster.
- Values close to +1 mean well-separated clusters, near 0 means overlapping, negative means misassigned.
- The overall score is the mean across all points.
Check yourself — multiple choice
- It measures how well a supervised model fits
- It measures cluster cohesion vs separation, values in [-1, 1]
- Higher magnitude negative values are best
- It only works for k-means
Silhouette combines intra-cluster tightness and inter-cluster separation.
#clustering#evaluation
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