Cluster validation: Silhouette score, Davies-Bouldin score, Calinski-Harabasz score - [Python]

Silhouette score, Davies-Bouldin score and Calinski-Harabasz score are all internal cluster validation techniques. They can determine the goodness of clustering algorithms without external references. In the case of the Silhouette score and Calinski-Harabasz score, a higher value denotes better clustering. On the other hand, a lower value of the Davies-Bouldin score indicates better clustering. We can easily calculate these metrics using built-in packages that are available in the scikit-learn library. GitHub address: https://github.com/randomaccess2023/MG2023/tree/main/Video%206 Helpful link: https://scikit-learn.org/stable/modules/clustering.htm #clustering-performance-evaluation Silhouette score (theory): https://en.wikipedia.org/wiki/Silhouette_(clustering) Davies-Bouldin score (theory): https://en.wikipedia.org/wiki/Davies%E2%80%93Bouldin_index Calinski-Harabasz score: https://www.geeksforgeeks.org/calinski-harabasz-index-cluster-validity-indices-set-3/ #data_science #jupyter_notebook #cluster_validation #python #sklearn

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