Machine Learning & Training

What Is Principal Component Analysis?

Principal component analysis (PCA) is a technique that reduces the dimensionality of data by finding new axes, called principal components, that capture the most variance. By projecting data onto these components, PCA compresses information while retaining important structure. It is widely used for visualization, noise reduction, and preprocessing.