This method is used when we have a large number of predictor variables and some of them are highly correlated. It involves finding a low-dimensional subspace that captures the most variation in the data, and fitting a linear model in this subspace.
This method is used when we have a large number of predictor variables and some of them are highly correlated. It involves finding a low-dimensional subspace that captures the most variation in the data, and fitting a linear model in this subspace.