Data Cloud Video Hub

18. Building Supervised Clustering models and generating labels

Explore the fundamentals of clustering models in this session of the Einstein Studio demo series. Learn how clustering can identify natural segments among loan applicants, revealing insights that traditional classification methods might miss. The session covers essential steps in creating clustering models, variable selection, and algorithm options, including HDBSCAN and K-means, providing an overview of their applications in discovering distinct applicant groups. Key metrics for evaluating model performance and stability are also discussed, ensuring that users can effectively interpret and utilize the results.