What is the silhouette score and how is it used in clustering?
An example of an AI chatbot conversation about silhouette score usage in clustering analysis.
What is the silhouette score and how is it used in clustering?
The silhouette score is a metric used to evaluate the quality of clustering. It measures how similar an object is to its own cluster compared to other clusters.
How is the silhouette score calculated?
The silhouette score is calculated using the formula: (b - a) / max(a, b), where 'a' is the average distance between a sample and all other points in the same cluster, and 'b' is the average distance to the nearest cluster.
What is a good silhouette score value?
A silhouette score ranges from -1 to 1. A score close to 1 indicates that the samples are well clustered, while a score near 0 suggests overlapping clusters.
Can you give an example of using silhouette score in Python?
Sure! You can use the silhouette_score function from the sklearn.metrics module in Python to compute it after clustering your data.
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