In machine learning, PCA stands for Principal Component Analysis. PCA is used to tackle a known ML problem when a dataset has a large number of features, i.e. high dimensions, also known as the curse of dimensionality. The ML feature engineering engineering techniques available are classified into feature selection and feature extraction techniques. Dimensionality reduction is a type of feature extraction technique which aims to reduce the number of input features in an ML model but at the same time retain the original information to the extent possible. PCA is a common dimensionality reduction technique.

PCA is used in machine learning at the data preparation stage and ml model training stage.

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