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Which evaluation metric is best for assessing clustering algorithms?

Practice Questions

Q1
Which evaluation metric is best for assessing clustering algorithms?
  1. Accuracy
  2. Silhouette Score
  3. Mean Squared Error
  4. F1 Score

Questions & Step-by-Step Solutions

Which evaluation metric is best for assessing clustering algorithms?
  • Step 1: Understand what clustering is. Clustering is a way to group similar items together.
  • Step 2: Know that we need a way to measure how good our clusters are.
  • Step 3: Learn about the Silhouette Score. It helps us see how well each item fits in its cluster.
  • Step 4: The Silhouette Score ranges from -1 to 1. A score close to 1 means the item is well placed in its cluster.
  • Step 5: Compare the Silhouette Score of different clustering results to find the best one.
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