About this document
Understanding Principal Component Analysis by kaustub is a document available to read on EtoBox.
Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms a large set of variables into a smaller set while preserving most of the information. It is particularly useful for datasets with many features, helping to reduce model training time, improve visualization, and mitigate issues like overfitting. PCA works by identifying principal components that capture maximum variance in the data, allowing for effective feature selection and easier data interpretation.
- Author
- kaustub
- Language
- EN