Principal Component Analysis (PCA)
Principal Component Analysis (PCA) is a widely used dimensionality reduction technique in machine learning and data processing. The primary goal of PCA is to reduce the number of features while preserving as much information as possible. This is achieved by finding new axes that maximize the variance in the data.
Introduction
Principal Component Analysis (PCA) is a widely used dimensionality reduction technique in machine learning and data processing. The primary goal of PCA is to reduce the number of features while preserving as much information as possible. This is achieved by finding new axes that maximize the variance in the data.
