Principal Component Analysis (PCA)
AI/ML Fundamentals
Dimensionality reduction technique
What is Principal Component Analysis (PCA)?
Transforms data to new coordinates (principal components) capturing maximum variance. Reduces dimensions while preserving information.
Real-World Examples
- •Reducing 1000 features to 10
- •Visualizing high-dimensional data
- •Noise reduction
When to Use This
To reduce features while retaining most information
Related Terms
Learn more about concepts related to Principal Component Analysis (PCA)