Feature Importance in Clustering
Explore how to assess and interpret feature importance in clustering algorithms, enabling a deeper understanding of the factors that influence the formation of clusters in unsupervised learning.
Partial Dependence Plots
Explore the role of Partial Dependence Plots (PDPs) in unsupervised learning. Understand how PDPs can provide insights into the relationship between features and clustering outcomes, aiding in the interpretation and explainability of complex models.
Understanding Clusters in Unsupervised Learning
Delve into techniques for interpreting clusters in unsupervised learning. Learn how to use centroid analysis, cluster profiles, and advanced visualization tools like t-SNE, UMAP, and heatmaps to gain actionable insights from your clustering results.
Advanced Cluster Validation Techniques
Delve into sophisticated metrics and methods for evaluating the quality of clustering outcomes. Learn about the Dunn Index, Davies-Bouldin Index, Calinski-Harabasz Index, and external validation methods to rigorously assess cluster performance.