Which evaluation metric is NOT typically used for clustering algorithms?

Which evaluation metric is NOT typically used for clustering algorithms?

Step 1: Understand what clustering algorithms do. They group similar data points together without using labeled data.
Step 2: Know that clustering is a type of unsupervised learning, meaning there are no predefined categories or labels for the data.
Step 3: Learn about evaluation metrics. These are ways to measure how well a model performs.
Step 4: Identify common evaluation metrics for clustering, such as silhouette score, Davies-Bouldin index, and inertia.
Step 5: Recognize that accuracy is a metric used in supervised learning, where you compare predicted labels to actual labels.
Step 6: Conclude that accuracy is not applicable to clustering because there are no actual labels to compare against.

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