Recall & Review
beginner
What is the purpose of cluster evaluation metrics?
Cluster evaluation metrics help us measure how well a clustering algorithm has grouped data points. They tell us if clusters are tight and well-separated.
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beginner
Explain the Silhouette Score in clustering.
The Silhouette Score measures how similar a point is to its own cluster compared to other clusters. Scores range from -1 to 1, where a higher score means better clustering.
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intermediate
What does the Davies-Bouldin Index indicate?
The Davies-Bouldin Index measures the average similarity between clusters. Lower values mean clusters are more distinct and better separated.
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intermediate
How does the Calinski-Harabasz Index evaluate clusters?
The Calinski-Harabasz Index compares the variance within clusters to the variance between clusters. Higher values indicate better defined clusters.
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intermediate
What is the difference between internal and external cluster evaluation metrics?
Internal metrics use only the data and cluster labels to evaluate quality (e.g., Silhouette Score). External metrics compare clustering results to known true labels (e.g., Adjusted Rand Index).
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Which cluster evaluation metric ranges from -1 to 1 and measures how well points fit their clusters?
✗ Incorrect
The Silhouette Score ranges from -1 to 1 and measures how similar points are to their own cluster compared to others.
A lower value of which metric indicates better cluster separation?
✗ Incorrect
The Davies-Bouldin Index measures cluster similarity; lower values mean clusters are more distinct.
Which metric compares variance within clusters to variance between clusters?
✗ Incorrect
The Calinski-Harabasz Index evaluates clustering by comparing within-cluster and between-cluster variance.
Which cluster evaluation metric requires true labels to compare clustering results?
✗ Incorrect
The Adjusted Rand Index is an external metric that compares clustering to true labels.
Internal cluster evaluation metrics use:
✗ Incorrect
Internal metrics evaluate clusters using only the data and the cluster assignments.
Describe the Silhouette Score and how it helps evaluate clusters.
Think about how close points are to their own cluster versus others.
You got /3 concepts.
Explain the difference between internal and external cluster evaluation metrics.
Consider whether true labels are needed.
You got /3 concepts.