Table Of Contents¶
- Welcome to scikit-learn
- scikit-learn Tutorials
- Getting Started
- User Guide
- Glossary of Common Terms and API Elements
- Examples
- Release Highlights
- Biclustering
- Calibration
- Classification
- Clustering
- Covariance estimation
- Cross decomposition
- Dataset examples
- Decision Trees
- Decomposition
- Ensemble methods
- Examples based on real world datasets
- Feature Selection
- Gaussian Mixture Models
- Gaussian Process for Machine Learning
- Generalized Linear Models
- Inspection
- Manifold learning
- Miscellaneous
- Missing Value Imputation
- Model Selection
- Multioutput methods
- Nearest Neighbors
- Neural Networks
- Pipelines and composite estimators
- Preprocessing
- Semi Supervised Classification
- Support Vector Machines
- Tutorial exercises
- Working with text documents
- API Reference
sklearn.base: Base classes and utility functionssklearn.calibration: Probability Calibrationsklearn.cluster: Clusteringsklearn.compose: Composite Estimatorssklearn.covariance: Covariance Estimatorssklearn.cross_decomposition: Cross decompositionsklearn.datasets: Datasetssklearn.decomposition: Matrix Decompositionsklearn.discriminant_analysis: Discriminant Analysissklearn.dummy: Dummy estimatorssklearn.ensemble: Ensemble Methodssklearn.exceptions: Exceptions and warningssklearn.experimental: Experimentalsklearn.feature_extraction: Feature Extractionsklearn.feature_selection: Feature Selectionsklearn.gaussian_process: Gaussian Processessklearn.impute: Imputesklearn.inspection: inspectionsklearn.isotonic: Isotonic regressionsklearn.kernel_approximationKernel Approximationsklearn.kernel_ridgeKernel Ridge Regressionsklearn.linear_model: Linear Modelssklearn.manifold: Manifold Learningsklearn.metrics: Metricssklearn.mixture: Gaussian Mixture Modelssklearn.model_selection: Model Selectionsklearn.multiclass: Multiclass and multilabel classificationsklearn.multioutput: Multioutput regression and classificationsklearn.naive_bayes: Naive Bayessklearn.neighbors: Nearest Neighborssklearn.neural_network: Neural network modelssklearn.pipeline: Pipelinesklearn.preprocessing: Preprocessing and Normalizationsklearn.random_projection: Random projectionsklearn.semi_supervisedSemi-Supervised Learningsklearn.svm: Support Vector Machinessklearn.tree: Decision Treessklearn.utils: Utilities- Recently deprecated
- Developer’s Guide