Meaning of PCA (Principal Component Analysis)

Simple definition

Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms high-dimensional data into fewer dimensions while preserving as much variance as possible.

How to use PCA (Principal Component Analysis) in a professional context

PCA is used in machine learning and data analysis to simplify datasets, reduce noise, and improve model performance.

Concrete example of PCA (Principal Component Analysis)

A data scientist applies PCA to compress a dataset with 100 features into 10 principal components, reducing computation time for a classification model.