3. Supervised learning¶
- 3.1. Generalized Linear Models
- 3.1.1. Ordinary Least Squares
- 3.1.2. Ridge Regression
- 3.1.3. Lasso
- 3.1.4. Elastic Net
- 3.1.5. Least Angle Regression
- 3.1.6. LARS Lasso
- 3.1.7. Orthogonal Matching Pursuit (OMP)
- 3.1.8. Bayesian Regression
- 3.1.9. Automatic Relevance Determination - ARD
- 3.1.10. Logisitic regression
- 3.1.11. Stochastic Gradient Descent - SGD
- 3.2. Support Vector Machines
- 3.3. Stochastic Gradient Descent
- 3.4. Nearest Neighbors
- 3.5. Gaussian Processes
- 3.6. Partial Least Squares
- 3.7. Naive Bayes
- 3.8. Multiclass algorithms
- 3.9. Feature selection