1. Supervised learning¶
- 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 regression: outliers and modeling errors
- 1.1.15. Polynomial regression: extending linear models with basis functions
- 1.2. Linear and Quadratic Discriminant Analysis
- 1.3. Kernel ridge regression
- 1.4. Support Vector Machines
- 1.5. Stochastic Gradient Descent
- 1.6. Nearest Neighbors
- 1.7. Gaussian Processes
- 1.8. Cross decomposition
- 1.9. Naive Bayes
- 1.10. Decision Trees
- 1.11. Ensemble methods
- 1.11.1. Bagging meta-estimator
- 1.11.2. Forests of randomized trees
- 1.11.3. AdaBoost
- 1.11.4. Gradient Tree Boosting
- 1.11.5. VotingClassifier
- 1.12. Multiclass and multilabel algorithms
- 1.13. Feature selection
- 1.14. Semi-Supervised
- 1.15. Isotonic regression
- 1.16. Probability calibration