Table Of Contents¶
- Welcome to scikit-learn
- scikit-learn Tutorials
- Getting Started
- User Guide
- Glossary of Common Terms and API Elements
- Examples
- Miscellaneous examples
- Examples based on real world datasets
- Biclustering
- Calibration
- Classification
- Clustering
- Pipelines and composite estimators
- Covariance estimation
- Cross decomposition
- Dataset examples
- Decomposition
- Ensemble methods
- Tutorial exercises
- Feature Selection
- Gaussian Process for Machine Learning
- Missing Value Imputation
- Inspection
- Generalized Linear Models
- Manifold learning
- Gaussian Mixture Models
- Model Selection
- Multioutput methods
- Nearest Neighbors
- Neural Networks
- Preprocessing
- Semi Supervised Classification
- Support Vector Machines
- Working with text documents
- Decision Trees
- API Reference
sklearn.base
: Base classes and utility functionssklearn.calibration
: Probability Calibrationsklearn.cluster
: Clusteringsklearn.cluster.bicluster
: Biclusteringsklearn.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_approximation
Kernel Approximationsklearn.kernel_ridge
Kernel Ridge Regressionsklearn.linear_model
: Generalized 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_supervised
Semi-Supervised Learningsklearn.svm
: Support Vector Machinessklearn.tree
: Decision Treessklearn.utils
: Utilities- Recently deprecated
- Developer’s Guide