In evaluating clustering algorithms, which metric assesses the compactness of cl

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Question: In evaluating clustering algorithms, which metric assesses the compactness of clusters?

Options:

Correct Answer: Silhouette Score

Solution:

Silhouette Score measures how similar an object is to its own cluster compared to other clusters.

In evaluating clustering algorithms, which metric assesses the compactness of clusters?

In evaluating clustering algorithms, which metric assesses the compactness of clusters?

Step 1: Understand what clustering algorithms do. They group similar data points together.
Step 2: Know that we want to evaluate how well these groups (clusters) are formed.
Step 3: Learn about the Silhouette Score, which is a metric used for this evaluation.
Step 4: The Silhouette Score measures how close a data point is to points in its own cluster versus points in other clusters.
Step 5: A high Silhouette Score means the data point is well matched to its own cluster and poorly matched to neighboring clusters.

Silhouette Score – A metric that evaluates the compactness and separation of clusters by comparing the average distance between points in the same cluster to the average distance to points in the nearest cluster.