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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
8. Tree-Based Methods
Notes
Exercises
1-6. Conceptual Exercises
7. Plotting test error for parameter values of random forest model in chapter 8 lab
8. Predicting
Sales
in
Carseats
dataset
9. Using tree based methods on the
OJ
dataset
10. Boosting to predict
Salary
in
Hitters
dataset
11. Predicting
Purchase
in
Caravan
dataset with a boosted tree classifier