Dimensionality Reduction 

A technique to reduce the number of features in a dataset while preserving its essential information.

What is Dimensionality Reduction?

– Improves computational efficiency – Reduces noise and redundancy – Improves model performance – Facilitates visualization

Why is it Important?

– Principal Component Analysis (PCA) – Linear Discriminant Analysis (LDA) – t-SNE – UMAP

Popular Techniques

– Find the principal components (directions of maximum variance). – Project the data onto these components.

How PCA Works

– Image and video processing – Natural language processing – Bioinformatics – Customer segmentation

Applications of Dimensionality Reduction

Consider factors like: – Type of data (numerical, categorical) – Goal of analysis (visualization, classification) – Computational resources

Choosing the Right Technique

Unlock the power of data simplification and gain valuable insights.

Master Dimensionality Reduction