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Assignment #5 - Model Selection & Regularization
This report explores key statistical learning methods including ridge regression, lasso, principal components regression (PCR), partial least squares (PLS), and best subset selection. The analysis examines the trade-offs between model complexity, interpretability, and prediction accuracy. Using the College and Boston datasets from the ISLR2 package, various models are evaluated using validation set and cross-validation test errors. The report emphasizes model selection, performance comparison, and the interpretability of regression models in predicting college applications and crime rates.