Welcome to scikit-learn
Installing scikit-learn
Frequently Asked Questions
Support
Related Projects
About us
Who is using scikit-learn?
Release History
Version 0.20 (under development)
Version 0.19.1
Version 0.19
Previous Releases
scikit-learn Tutorials
An introduction to machine learning with scikit-learn
A tutorial on statistical-learning for scientific data processing
Working With Text Data
Choosing the right estimator
External Resources, Videos and Talks
User Guide
Supervised learning
Unsupervised learning
Model selection and evaluation
Dataset transformations
Dataset loading utilities
Strategies to scale computationally: bigger data
Computational Performance
Glossary of Common Terms and API Elements
General Concepts
Class APIs and Estimator Types
Target Types
Methods
Parameters
Attributes
Data and sample properties
Examples
General examples
Examples based on real world datasets
Biclustering
Calibration
Classification
Clustering
Covariance estimation
Cross decomposition
Dataset examples
Decomposition
Ensemble methods
Tutorial exercises
Feature Selection
Gaussian Process for Machine Learning
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 functions
sklearn.calibration: Probability Calibration
sklearn.cluster: Clustering
sklearn.cluster.bicluster: Biclustering
sklearn.compose: Composite Estimators
sklearn.covariance: Covariance Estimators
sklearn.cross_decomposition: Cross decomposition
sklearn.datasets: Datasets
sklearn.decomposition: Matrix Decomposition
sklearn.discriminant_analysis: Discriminant Analysis
sklearn.dummy: Dummy estimators
sklearn.ensemble: Ensemble Methods
sklearn.exceptions: Exceptions and warnings
sklearn.feature_extraction: Feature Extraction
sklearn.feature_selection: Feature Selection
sklearn.gaussian_process: Gaussian Processes
sklearn.isotonic: Isotonic regression
sklearn.impute: Impute
sklearn.kernel_approximation Kernel Approximation
sklearn.kernel_ridge Kernel Ridge Regression
sklearn.linear_model: Generalized Linear Models
sklearn.manifold: Manifold Learning
sklearn.metrics: Metrics
sklearn.mixture: Gaussian Mixture Models
sklearn.model_selection: Model Selection
sklearn.multiclass: Multiclass and multilabel classification
sklearn.multioutput: Multioutput regression and classification
sklearn.naive_bayes: Naive Bayes
sklearn.neighbors: Nearest Neighbors
sklearn.neural_network: Neural network models
sklearn.pipeline: Pipeline
sklearn.preprocessing: Preprocessing and Normalization
sklearn.random_projection: Random projection
sklearn.semi_supervised Semi-Supervised Learning
sklearn.svm: Support Vector Machines
sklearn.tree: Decision Trees
sklearn.utils: Utilities
Recently deprecated
Developer’s Guide
Contributing
Developers’ Tips and Tricks
Utilities for Developers
How to optimize for speed
Advanced installation instructions
Maintainer / core-developer information
Bibliography
Index