This is documentation for an old release of Scikit-learn (version 1.3). Try the latest stable release (version 1.6) or development (unstable) versions.
User Guide¶
- 1. Supervised learning
- 1.1. Linear Models
- 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)
- 2. Unsupervised learning
- 3. Model selection and evaluation
- 4. Inspection
- 5. Visualizations
- 6. Dataset transformations
- 6.1. Pipelines and composite estimators
- 6.2. Feature extraction
- 6.3. Preprocessing data
- 6.4. Imputation of missing values
- 6.5. Unsupervised dimensionality reduction
- 6.6. Random Projection
- 6.7. Kernel Approximation
- 6.8. Pairwise metrics, Affinities and Kernels
- 6.9. Transforming the prediction target (
y
)
- 7. Dataset loading utilities
- 8. Computing with scikit-learn
- 9. Model persistence
- 10. Common pitfalls and recommended practices
- 11. Dispatching