Mindforge ML | Unit 5 – Podcast 05_Title: PCA and Clustering Evaluation Techniques

This episode concludes Unit 5 by exploring dimensionality reduction and methods to evaluate clustering performance.

Key topics:

• Dimensionality reduction: Handling high-dimensional data.

• Principal Component Analysis (PCA): Variance-based transformation.

• WCSS: Measuring cluster compactness.

• Silhouette score: Evaluating cluster separation.

• Calinski-Harabasz index: Cluster quality measurement.

This episode completes the journey of unsupervised learning by connecting concepts with evaluation techniques.

Series: Mindforge ML

Produced by: Chatake Innoworks Pvt. Ltd.

Initiative: MindforgeAI