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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
6. Linear Model Selection and Regularization
Notes
Exercises
1-7. Conceptual Exercises
8. Feature Selection on Simulated Data
9. Predicting
Apps
in
College
dataset
10. Exploring test error on a simulated dataset
11. Predicting
crim
in
Boston
dataset