Pca in machine learning: Principal Component Analysis ( PCA

Principal Component Analysis ( PCA ) is a dimensionality reduction technique used in machine learning and data analysis. It transforms large datasets with many features into smaller sets while keeping the most important information. Principal Component Analysis ( PCA ) is a powerful technique in the field of machine learning and data science. It’s widely used for dimensionality reduction, data compression, and feature ... PCA (Principal Component Analysis) is a dimensionality reduction technique used in data analysis and machine learning . It helps you to reduce the number of features in a dataset while keeping the most important information.