1. Supervised learning

• 1.1. Linear Models
  • 1.1.1. Ordinary Least Squares
  • 1.1.2. Ridge regression and classification
  • 1.1.3. Lasso
  • 1.1.4. Multi-task Lasso
  • 1.1.5. Elastic-Net
  • 1.1.6. Multi-task Elastic-Net
  • 1.1.7. Least Angle Regression
  • 1.1.8. LARS Lasso
  • 1.1.9. Orthogonal Matching Pursuit (OMP)
  • 1.1.10. Bayesian Regression
  • 1.1.11. Logistic regression
  • 1.1.12. Stochastic Gradient Descent - SGD
  • 1.1.13. Perceptron
  • 1.1.14. Passive Aggressive Algorithms
  • 1.1.15. Robustness regression: outliers and modeling errors
  • 1.1.16. Polynomial regression: extending linear models with basis functions
• 1.2. Linear and Quadratic Discriminant Analysis
  • 1.2.1. Dimensionality reduction using Linear Discriminant Analysis
  • 1.2.2. Mathematical formulation of the LDA and QDA classifiers
  • 1.2.3. Mathematical formulation of LDA dimensionality reduction
  • 1.2.4. Shrinkage
  • 1.2.5. Estimation algorithms
• 1.3. Kernel ridge regression
• 1.4. Support Vector Machines
  • 1.4.1. Classification
  • 1.4.2. Regression
  • 1.4.3. Density estimation, novelty detection
  • 1.4.4. Complexity
  • 1.4.5. Tips on Practical Use
  • 1.4.6. Kernel functions
  • 1.4.7. Mathematical formulation
  • 1.4.8. Implementation details
• 1.5. Stochastic Gradient Descent
  • 1.5.1. Classification
  • 1.5.2. Regression
  • 1.5.3. Stochastic Gradient Descent for sparse data
  • 1.5.4. Complexity
  • 1.5.5. Stopping criterion
  • 1.5.6. Tips on Practical Use
  • 1.5.7. Mathematical formulation
  • 1.5.8. Implementation details
• 1.6. Nearest Neighbors
  • 1.6.1. Unsupervised Nearest Neighbors
  • 1.6.2. Nearest Neighbors Classification
  • 1.6.3. Nearest Neighbors Regression
  • 1.6.4. Nearest Neighbor Algorithms
  • 1.6.5. Nearest Centroid Classifier
  • 1.6.6. Nearest Neighbors Transformer
  • 1.6.7. Neighborhood Components Analysis
• 1.7. Gaussian Processes
  • 1.7.1. Gaussian Process Regression (GPR)
  • 1.7.2. GPR examples
  • 1.7.3. Gaussian Process Classification (GPC)
  • 1.7.4. GPC examples
  • 1.7.5. Kernels for Gaussian Processes
• 1.8. Cross decomposition
• 1.9. Naive Bayes
  • 1.9.1. Gaussian Naive Bayes
  • 1.9.2. Multinomial Naive Bayes
  • 1.9.3. Complement Naive Bayes
  • 1.9.4. Bernoulli Naive Bayes
  • 1.9.5. Categorical Naive Bayes
  • 1.9.6. Out-of-core naive Bayes model fitting
• 1.10. Decision Trees
  • 1.10.1. Classification
  • 1.10.2. Regression
  • 1.10.3. Multi-output problems
  • 1.10.4. Complexity
  • 1.10.5. Tips on practical use
  • 1.10.6. Tree algorithms: ID3, C4.5, C5.0 and CART
  • 1.10.7. Mathematical formulation
  • 1.10.8. Minimal Cost-Complexity Pruning
• 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. Histogram-Based Gradient Boosting
  • 1.11.6. Voting Classifier
  • 1.11.7. Voting Regressor
  • 1.11.8. Stacked generalization
• 1.12. Multiclass and multilabel algorithms
  • 1.12.1. Multilabel classification format
  • 1.12.2. One-Vs-The-Rest
  • 1.12.3. One-Vs-One
  • 1.12.4. Error-Correcting Output-Codes
  • 1.12.5. Multioutput regression
  • 1.12.6. Multioutput classification
  • 1.12.7. Classifier Chain
  • 1.12.8. Regressor Chain
• 1.13. Feature selection
  • 1.13.1. Removing features with low variance
  • 1.13.2. Univariate feature selection
  • 1.13.3. Recursive feature elimination
  • 1.13.4. Feature selection using SelectFromModel
  • 1.13.5. Feature selection as part of a pipeline
• 1.14. Semi-Supervised
  • 1.14.1. Label Propagation
• 1.15. Isotonic regression
• 1.16. Probability calibration
• 1.17. Neural network models (supervised)
  • 1.17.1. Multi-layer Perceptron
  • 1.17.2. Classification
  • 1.17.3. Regression
  • 1.17.4. Regularization
  • 1.17.5. Algorithms
  • 1.17.6. Complexity
  • 1.17.7. Mathematical formulation
  • 1.17.8. Tips on Practical Use
  • 1.17.9. More control with warm_start