1. Supervised learningΒΆ
- 1.1. Generalized Linear Models
- 1.1.1. Ordinary Least Squares
- 1.1.2. Ridge Regression
- 1.1.3. Lasso
- 1.1.4. Elastic Net
- 1.1.5. Multi-task Lasso
- 1.1.6. Least Angle Regression
- 1.1.7. LARS Lasso
- 1.1.8. Orthogonal Matching Pursuit (OMP)
- 1.1.9. Bayesian Regression
- 1.1.10. Logistic regression
- 1.1.11. Stochastic Gradient Descent - SGD
- 1.1.12. Perceptron
- 1.1.13. Passive Aggressive Algorithms
- 1.1.14. Robustness to outliers: RANSAC
- 1.1.15. Polynomial Regression: Extending Linear Models with Basis Functions
- 1.2. Support Vector Machines
- 1.3. Stochastic Gradient Descent
- 1.4. Nearest Neighbors
- 1.5. Gaussian Processes
- 1.6. Cross decomposition
- 1.7. Naive Bayes
- 1.8. Decision Trees
- 1.9. Ensemble methods
- 1.10. Multiclass and multilabel algorithms
- 1.11. Feature selection
- 1.12. Semi-Supervised
- 1.13. Linear and quadratic discriminant analysis
- 1.14. Isotonic regression