Supervised & Unsupervised Learning › Hierarchical Clustering

What are common mistakes to avoid with Hierarchical Clustering

Intermediate

Answer

A common mistake with Hierarchical Clustering is applying it without fully understanding the underlying data or problem, which often leads to weak or misleading results. Skipping proper testing before relying on it in a real project is another frequent error.

Real-world example A biologist uses hierarchical clustering to organize different species into a tree based on genetic similarity.

Common follow-ups: What is Supervised & Unsupervised Learning, How is Hierarchical Clustering evaluated in practice, What tools are commonly used for Hierarchical Clustering

Supervised & Unsupervised Learning topics: Introduction to Supervised Learning Introduction to Unsupervised Learning Classification Algorithms