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Install
User Guide
API
Examples
Community
Getting Started
Release History
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Development
FAQ
Support
Related Projects
Roadmap
Governance
About us
GitHub
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Section Navigation
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
2.1. Gaussian mixture models
2.2. Manifold learning
2.3. Clustering
2.4. Biclustering
2.5. Decomposing signals in components (matrix factorization problems)
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
3.1. Cross-validation: evaluating estimator performance
3.2. Tuning the hyper-parameters of an estimator
3.3. Tuning the decision threshold for class prediction
3.4. Metrics and scoring: quantifying the quality of predictions
3.5. Validation curves: plotting scores to evaluate models
4. Metadata Routing
5. Inspection
5.1. Partial Dependence and Individual Conditional Expectation plots
5.2. Permutation feature importance
6. Visualizations
7. Dataset transformations
7.1. Pipelines and composite estimators
7.2. Feature extraction
7.3. Preprocessing data
7.4. Imputation of missing values
7.5. Unsupervised dimensionality reduction
7.6. Random Projection
7.7. Kernel Approximation
