Principal Component Regression (PCR)

Principle Component Regression (PCR) is an algorithm for reducing the multi-collinearity of a dataset. The problem we face in multi-variate linear regression (linear regression with a large number of features) is that although it may appear that we do fit the model well, there is normally a high-variance problem on the test set.

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