Which evaluation metric is NOT typically used for clustering?

Which evaluation metric is NOT typically used for clustering?

Step 1: Understand what clustering is. Clustering is a method of grouping similar items together based on their features.
Step 2: Learn about evaluation metrics. Evaluation metrics are used to measure how well a model performs.
Step 3: Identify common evaluation metrics for clustering. Common metrics include Silhouette Score, Davies-Bouldin Index, and Adjusted Rand Index.
Step 4: Recognize the F1 Score. The F1 Score is a metric used to evaluate classification tasks, which involve predicting categories.
Step 5: Conclude that F1 Score is not used for clustering. Since clustering does not involve predefined categories, the F1 Score is not applicable.

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