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2. Statistical Learning
3. Linear Regression
4. Logistic Regression
5. Resampling Methods
6. Linear Model Selection and Regularization
7. Moving Beyond Linearity
8. Tree-Based Methods
9. Support Vector Machines
10. Unsupervised Learning
© 2019.
islr
notes and exercises from An Introduction to Statistical Learning
7. Moving Beyond Linearity
Notes
Exercises
1-5. Conceptual Exercises
6. Using polynomial and step function regression to predict
wage
using
age
in
Wage
dataset
7. Using non-linear multiple regression to predict
wage
in
Wage
dataset
8. Investigating non-linear relationships in
Auto
dataset
9. Nonlinear models for predicting
nox
using
dis
in
Boston
dataset
10. Predicting
Outstate
in
College
dataset with FSS and GAM
11. Exploring backfitting for multiple linear regression