Principle Component Analysis (PCA) Mathematics

Principal Component Analysis (PCA) is a dimensionality reduction technique used to reduce N features to P features while retaining as much variance as possible. Standardization of data is crucial before applying PCA to prevent variance-dominated principal components. PCA benefits include improved model performance and reduced overfitting, but it also has downsides such as loss of interpretability and lossy compression.