Feature Engineering for Modern Machine Learning with Scikit-Learn

3. Evaluating Clustering Results

After performing clustering, it's crucial to evaluate the quality and meaningfulness of the resulting clusters. This evaluation process is essential to ensure that the segmentation provides actionable insights for business strategies. Unlike supervised learning, where we have predefined labels to compare against, clustering evaluation relies on internal metrics that assess the structure of the clusters themselves.

These evaluation metrics typically focus on two key aspects:

• Internal cohesion: This measures how similar the data points within each cluster are to one another. High internal cohesion indicates that the points in a cluster are closely related and share common characteristics.
• Separation between clusters: ...