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 Principal Component Regression (PCR) is a **dimensionality reduction** technique that combines **Principal Component Analysis (PCA)** and regression. It first extracts the principal components of the predictors and then performs a linear regression on these components. PCR is a **supervised learning** method that can be used to improve the performance of regression models by reducing the number of predictors and removing multicollinearity.
 
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 ## Principal Component Regression: Introduction