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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
9. Support Vector Machines
Notes
Exercises
4. Comparing polynomial, radial, and linear kernel SVMs on a simulated dataset
5. Nonlinear decision boundary with logistic regression and nonlinear feature transformation
6. SVCs for barely linearly separable data
7. Using SVMs to classify mileage in
Auto
dataset
8. Using SVMs to classify
Purchase
in
OJ
dataset