User Guide#
- 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. Generalized Linear Models
- 1.1.13. Stochastic Gradient Descent - SGD
- 1.1.14. Robustness regression: outliers and modeling errors
- 1.1.15. Quantile Regression
- 1.1.16. 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. Ensembles: Gradient boosting, random forests, bagging, voting, stacking
- 1.12. Multiclass and multioutput algorithms
- 1.13. Feature selection
- 1.14. Semi-supervised learning
- 1.15. Isotonic regression
- 1.16. Probability calibration
- 1.17. Neural network models (supervised)
- 1.1. Linear Models
- 2. Unsupervised learning- 2.1. Gaussian mixture models
- 2.2. Manifold learning- 2.2.1. Introduction
- 2.2.2. Isomap
- 2.2.3. Locally Linear Embedding
- 2.2.4. Modified Locally Linear Embedding
- 2.2.5. Hessian Eigenmapping
- 2.2.6. Spectral Embedding
- 2.2.7. Local Tangent Space Alignment
- 2.2.8. Multi-dimensional Scaling (MDS)
- 2.2.9. t-distributed Stochastic Neighbor Embedding (t-SNE)
- 2.2.10. Tips on practical use
- 2.3. Clustering
- 2.4. Biclustering
- 2.5. Decomposing signals in components (matrix factorization problems)- 2.5.1. Principal component analysis (PCA)
- 2.5.2. Kernel Principal Component Analysis (kPCA)
- 2.5.3. Truncated singular value decomposition and latent semantic analysis
- 2.5.4. Dictionary Learning
- 2.5.5. Factor Analysis
- 2.5.6. Independent component analysis (ICA)
- 2.5.7. Non-negative matrix factorization (NMF or NNMF)
- 2.5.8. Latent Dirichlet Allocation (LDA)
- 2.6. Covariance estimation
- 2.7. Novelty and Outlier Detection
- 2.8. Density Estimation
- 2.9. Neural network models (unsupervised)
- 3. Model selection and evaluation
- 4. Metadata Routing
- 5. Inspection
- 6. Visualizations
- 7. Callbacks
- 8. Dataset transformations- 8.1. Pipelines and composite estimators
- 8.2. Feature extraction
- 8.3. Preprocessing data
- 8.4. Imputation of missing values
- 8.5. Unsupervised dimensionality reduction
- 8.6. Random Projection
- 8.7. Kernel Approximation
- 8.8. Pairwise metrics, Affinities and Kernels
- 8.9. Transforming the prediction target (y)
- 9. Dataset loading utilities
- 10. Computing with scikit-learn
- 11. Model persistence
- 12. Common pitfalls and recommended practices
- 13. Data Interoperability
- 14. Choosing the right estimator
- 15. External Resources, Videos and Talks