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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
View all episodes
By CI CodesmithThis 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