Table Of Contents¶

• Welcome to scikit-learn
  • Installing scikit-learn
  • Frequently Asked Questions
  • Support
  • Related Projects
  • About us
  • Who is using scikit-learn?
  • Release History
  • Roadmap
  • Scikit-learn governance and decision-making
• 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
• Getting Started
  • Fitting and predicting: estimator basics
  • Transformers and pre-processors
  • Pipelines: chaining pre-processors and estimators
  • Model evaluation
  • Automatic parameter searches
  • Next steps
• User Guide
  • 1. Supervised learning
  • 2. Unsupervised learning
  • 3. Model selection and evaluation
  • 4. Inspection
  • 5. Visualizations
  • 6. Dataset transformations
  • 7. Dataset loading utilities
  • 8. Computing with scikit-learn
  • 9. Model persistence
  • 10. Common pitfalls and recommended practices
• Glossary of Common Terms and API Elements
  • General Concepts
  • Class APIs and Estimator Types
  • Target Types
  • Methods
  • Parameters
  • Attributes
  • Data and sample properties
• 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
  • Kernel Approximation
  • 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 functions
  • sklearn.calibration: Probability Calibration
  • sklearn.cluster: Clustering
  • 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.experimental: Experimental
  • sklearn.feature_extraction: Feature Extraction
  • sklearn.feature_selection: Feature Selection
  • sklearn.gaussian_process: Gaussian Processes
  • sklearn.impute: Impute
  • sklearn.inspection: Inspection
  • sklearn.isotonic: Isotonic regression
  • sklearn.kernel_approximation: Kernel Approximation
  • sklearn.kernel_ridge: Kernel Ridge Regression
  • sklearn.linear_model: Linear Models
  • sklearn.manifold: Manifold Learning
  • sklearn.metrics: Metrics
  • sklearn.mixture: Gaussian Mixture Models
  • sklearn.model_selection: Model Selection
  • sklearn.multiclass: Multiclass 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
  • Developing scikit-learn estimators
  • Developers’ Tips and Tricks
  • Utilities for Developers
  • How to optimize for speed
  • Installing the development version of scikit-learn
  • Bug triaging and issue curation
  • Maintainer / core-developer information
  • Developing with the Plotting API