Proyecto 2 del curso de Modelacion y Simulacion 1 del CUNOC del estudiante Fernando Rodriguez 202030542

Maheswari Rahma Sarvidya 2304220019 Statistika dan Sains Data

ISLR Chapter 9 - Questions 5, 7 and 8

ISLR Chapter 8 - Questions 3, 8 and 9

This homework focuses on tuning and evaluating nonlinear regression models using both simulated and real-world datasets. We explore the impact of variable correlation on feature importance (8.1–8.3), investigate bias in tree-based models (8.4–8.6), and finally apply model tuning techniques to the Chemical Manufacturing Process dataset (8.7). Across the exercises, we rely heavily on the caret framework for resampling, model training, and performance evaluation. Techniques like bagging, boosting, and SVMs are compared using RMSE and R² to identify the most effective approach. Along the way, we also evaluate how model interpretation changes when predictors are duplicated or correlated.