Guide-linked quiz · Medium Level
Silhouette Score and Cluster Evaluation Quiz
Calculate per-point silhouette values and distinguish internal geometric diagnostics from cluster utility and alternative indices.
Question 1 of 8
With a=2 and b=5, what is the silhouette coefficient (b-a)/max(a,b)?
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- With a=2 and b=5, what is the silhouette coefficient (b-a)/max(a,b)?
- 0.6
- -0.6
- 0.4
- 1.5
- A point has a=4 and b=3. What does its negative silhouette indicate?
- Its assigned cluster has zero observations
- It is, on average, closer to another cluster under the chosen distance
- The global score must be negative
- The feature scaling is proven correct
- What does a silhouette near zero commonly suggest for one observation?
- Its class label is known
- It is perfectly separated from all clusters
- It lies near a boundary between its own and another cluster
- The clustering has one cluster only
- Which direction is generally preferred for the Davies-Bouldin index?
- A value matching the silhouette range
- Higher values
- Exactly zero for every useful partition
- Lower values
- Why should raw Davies-Bouldin and Calinski-Harabasz values not be compared directly?
- Both are probability estimates between zero and one
- They use different formulas, scales and preferred directions
- They require identical units by definition
- One is always the reciprocal of the other
- Why can an average silhouette hide a cluster-specific problem?
- Every sample receives a different formula
- Silhouette uses only labels and no distances
- A sample-weighted mean can mask weak values in a smaller group
- A negative value is omitted automatically
- What does a high silhouette score fail to establish by itself?
- That labels were assigned
- That within and between distances were calculated
- That the chosen metric was used
- That the clusters are useful for the downstream task
- When is the silhouette score undefined in common implementations?
- For degenerate labelings with one cluster or one cluster per sample
- Whenever the average coefficient is positive
- When a cluster has two observations
- When distances use standardized features
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