• scikit-learn Tutorials
  • An introduction to machine learning with scikit-learn
    • Machine learning: the problem setting
    • Loading an example dataset
    • Learning and predicting
    • Model persistence
  • A tutorial on statistical-learning for scientific data processing
    • Statistical learning: the setting and the estimator object in the scikit-learn
      • Datasets
      • Estimators objects
    • Supervised learning: predicting an output variable from high-dimensional observations
      • Nearest neighbor and the curse of dimensionality
        • k-Nearest neighbors classifier
        • The curse of dimensionality
      • Linear model: from regression to sparsity
        • Linear regression
        • Shrinkage
        • Sparsity
        • Classification
      • Support vector machines (SVMs)
        • Linear SVMs
        • Using kernels
    • Model selection: choosing estimators and their parameters
      • Score, and cross-validated scores
      • Cross-validation generators
      • Grid-search and cross-validated estimators
        • Grid-search
        • Cross-validated estimators
    • Unsupervised learning: seeking representations of the data
      • Clustering: grouping observations together
        • K-means clustering
        • Hierarchical agglomerative clustering: Ward
          • Connectivity-constrained clustering
          • Feature agglomeration
      • Decompositions: from a signal to components and loadings
        • Principal component analysis: PCA
        • Independent Component Analysis: ICA
    • Putting it all together
      • Pipelining
      • Face recognition with eigenfaces
      • Open problem: Stock Market Structure
    • Finding help
      • The project mailing list
      • Q&A communities with Machine Learning practitioners
  • Working With Text Data
    • Tutorial setup
    • Loading the 20 newgroups dataset
    • Extracting features from text files
      • Bags of words
      • Tokenizing text with scikit-learn
      • From occurrences to frequencies
    • Training a classifier
    • Building a pipeline
    • Evaluation of the performance on the test set
    • Parameter tuning using grid search
    • Exercise 1: Language identification
    • Exercise 2: Sentiment Analysis on movie reviews
    • Exercise 3: CLI text classification utility
    • Where to From Here
• 1. Supervised learning
  • 1.1. Generalized Linear Models
    • 1.1.1. Ordinary Least Squares
      • 1.1.1.1. Ordinary Least Squares Complexity
    • 1.1.2. Ridge Regression
      • 1.1.2.1. Ridge Complexity
      • 1.1.2.2. Setting the regularization parameter: generalized Cross-Validation
    • 1.1.3. Lasso
      • 1.1.3.1. Setting regularization parameter
        • 1.1.3.1.1. Using cross-validation
        • 1.1.3.1.2. Information-criteria based model selection
    • 1.1.4. Elastic Net
    • 1.1.5. Multi-task Lasso
    • 1.1.6. Least Angle Regression
    • 1.1.7. LARS Lasso
      • 1.1.7.1. Mathematical formulation
    • 1.1.8. Orthogonal Matching Pursuit (OMP)
    • 1.1.9. Bayesian Regression
      • 1.1.9.1. Bayesian Ridge Regression
      • 1.1.9.2. Automatic Relevance Determination - ARD
    • 1.1.10. Logistic regression
    • 1.1.11. Stochastic Gradient Descent - SGD
    • 1.1.12. Perceptron
    • 1.1.13. Passive Aggressive Algorithms
    • 1.1.14. Robustness to outliers: RANSAC
    • 1.1.15. Polynomial Regression: Extending Linear Models with Basis Functions
  • 1.2. Support Vector Machines
    • 1.2.1. Classification
      • 1.2.1.1. Multi-class classification
      • 1.2.1.2. Scores and probabilities
      • 1.2.1.3. Unbalanced problems
    • 1.2.2. Regression
    • 1.2.3. Density estimation, novelty detection
    • 1.2.4. Complexity
    • 1.2.5. Tips on Practical Use
    • 1.2.6. Kernel functions
      • 1.2.6.1. Custom Kernels
        • 1.2.6.1.1. Using Python functions as kernels
        • 1.2.6.1.2. Using the Gram matrix
        • 1.2.6.1.3. Parameters of the RBF Kernel
    • 1.2.7. Mathematical formulation
      • 1.2.7.1. SVC
      • 1.2.7.2. NuSVC
    • 1.2.8. Implementation details
  • 1.3. Stochastic Gradient Descent
    • 1.3.1. Classification
    • 1.3.2. Regression
    • 1.3.3. Stochastic Gradient Descent for sparse data
    • 1.3.4. Complexity
    • 1.3.5. Tips on Practical Use
    • 1.3.6. Mathematical formulation
      • 1.3.6.1. SGD
    • 1.3.7. Implementation details
  • 1.4. Nearest Neighbors
    • 1.4.1. Unsupervised Nearest Neighbors
      • 1.4.1.1. Finding the Nearest Neighbors
      • 1.4.1.2. KDTree and BallTree Classes
    • 1.4.2. Nearest Neighbors Classification
    • 1.4.3. Nearest Neighbors Regression
    • 1.4.4. Nearest Neighbor Algorithms
      • 1.4.4.1. Brute Force
      • 1.4.4.2. K-D Tree
      • 1.4.4.3. Ball Tree
      • 1.4.4.4. Choice of Nearest Neighbors Algorithm
      • 1.4.4.5. Effect of leaf_size
    • 1.4.5. Nearest Centroid Classifier
      • 1.4.5.1. Nearest Shrunken Centroid
  • 1.5. Gaussian Processes
    • 1.5.1. Examples
      • 1.5.1.1. An introductory regression example
      • 1.5.1.2. Fitting Noisy Data
    • 1.5.2. Mathematical formulation
      • 1.5.2.1. The initial assumption
      • 1.5.2.2. The best linear unbiased prediction (BLUP)
      • 1.5.2.3. The empirical best linear unbiased predictor (EBLUP)
    • 1.5.3. Correlation Models
    • 1.5.4. Regression Models
    • 1.5.5. Implementation details
  • 1.6. Cross decomposition
  • 1.7. Naive Bayes
    • 1.7.1. Gaussian Naive Bayes
    • 1.7.2. Multinomial Naive Bayes
    • 1.7.3. Bernoulli Naive Bayes
    • 1.7.4. Out-of-core naive Bayes model fitting
  • 1.8. Decision Trees
    • 1.8.1. Classification
    • 1.8.2. Regression
    • 1.8.3. Multi-output problems
    • 1.8.4. Complexity
    • 1.8.5. Tips on practical use
    • 1.8.6. Tree algorithms: ID3, C4.5, C5.0 and CART
    • 1.8.7. Mathematical formulation
      • 1.8.7.1. Classification criteria
      • 1.8.7.2. Regression criteria
  • 1.9. Ensemble methods
    • 1.9.1. Bagging meta-estimator
    • 1.9.2. Forests of randomized trees
      • 1.9.2.1. Random Forests
      • 1.9.2.2. Extremely Randomized Trees
      • 1.9.2.3. Parameters
      • 1.9.2.4. Parallelization
      • 1.9.2.5. Feature importance evaluation
      • 1.9.2.6. Totally Random Trees Embedding
    • 1.9.3. AdaBoost
      • 1.9.3.1. Usage
    • 1.9.4. Gradient Tree Boosting
      • 1.9.4.1. Classification
      • 1.9.4.2. Regression
      • 1.9.4.3. Mathematical formulation
        • 1.9.4.3.1. Loss Functions
      • 1.9.4.4. Regularization
        • 1.9.4.4.1. Shrinkage
        • 1.9.4.4.2. Subsampling
      • 1.9.4.5. Interpretation
        • 1.9.4.5.1. Feature importance
        • 1.9.4.5.2. Partial dependence
  • 1.10. Multiclass and multilabel algorithms
    • 1.10.1. Multilabel classification format
    • 1.10.2. One-Vs-The-Rest
      • 1.10.2.1. Multiclass learning
      • 1.10.2.2. Multilabel learning
    • 1.10.3. One-Vs-One
      • 1.10.3.1. Multiclass learning
    • 1.10.4. Error-Correcting Output-Codes
      • 1.10.4.1. Multiclass learning
  • 1.11. Feature selection
    • 1.11.1. Removing features with low variance
    • 1.11.2. Univariate feature selection
    • 1.11.3. Recursive feature elimination
    • 1.11.4. L1-based feature selection
      • 1.11.4.1. Selecting non-zero coefficients
      • 1.11.4.2. Randomized sparse models
    • 1.11.5. Tree-based feature selection
    • 1.11.6. Feature selection as part of a pipeline
  • 1.12. Semi-Supervised
    • 1.12.1. Label Propagation
  • 1.13. Linear and quadratic discriminant analysis
    • 1.13.1. Dimensionality reduction using LDA
    • 1.13.2. Mathematical Idea
  • 1.14. Isotonic regression
• 2. Unsupervised learning
  • 2.1. Gaussian mixture models
    • 2.1.1. GMM classifier
      • 2.1.1.1. Pros and cons of class GMM: expectation-maximization inference
        • 2.1.1.1.1. Pros
        • 2.1.1.1.2. Cons
      • 2.1.1.2. Selecting the number of components in a classical GMM
      • 2.1.1.3. Estimation algorithm Expectation-maximization
    • 2.1.2. VBGMM classifier: variational Gaussian mixtures
      • 2.1.2.1. Pros and cons of class VBGMM: variational inference
        • 2.1.2.1.1. Pros
        • 2.1.2.1.2. Cons
      • 2.1.2.2. Estimation algorithm: variational inference
    • 2.1.3. DPGMM classifier: Infinite Gaussian mixtures
      • 2.1.3.1. Pros and cons of class DPGMM: Diriclet process mixture model
        • 2.1.3.1.1. Pros
        • 2.1.3.1.2. Cons
      • 2.1.3.2. The Dirichlet Process
  • 2.2. Manifold learning
    • 2.2.1. Introduction
    • 2.2.2. Isomap
      • 2.2.2.1. Complexity
    • 2.2.3. Locally Linear Embedding
      • 2.2.3.1. Complexity
    • 2.2.4. Modified Locally Linear Embedding
      • 2.2.4.1. Complexity
    • 2.2.5. Hessian Eigenmapping
      • 2.2.5.1. Complexity
    • 2.2.6. Spectral Embedding
      • 2.2.6.1. Complexity
    • 2.2.7. Local Tangent Space Alignment
      • 2.2.7.1. Complexity
    • 2.2.8. Multi-dimensional Scaling (MDS)
      • 2.2.8.1. Metric MDS
      • 2.2.8.2. Nonmetric MDS
    • 2.2.9. Tips on practical use
  • 2.3. Clustering
    • 2.3.1. Overview of clustering methods
    • 2.3.2. K-means
      • 2.3.2.1. Mini Batch K-Means
    • 2.3.3. Affinity Propagation
    • 2.3.4. Mean Shift
    • 2.3.5. Spectral clustering
      • 2.3.5.1. Different label assignment strategies
    • 2.3.6. Hierarchical clustering
      • 2.3.6.1. Adding connectivity constraints
    • 2.3.7. DBSCAN
    • 2.3.8. Clustering performance evaluation
      • 2.3.8.1. Inertia
        • 2.3.8.1.1. Presentation and usage
        • 2.3.8.1.2. Advantages
        • 2.3.8.1.3. Drawbacks
      • 2.3.8.2. Adjusted Rand index
        • 2.3.8.2.1. Presentation and usage
        • 2.3.8.2.2. Advantages
        • 2.3.8.2.3. Drawbacks
        • 2.3.8.2.4. Mathematical formulation
      • 2.3.8.3. Mutual Information based scores
        • 2.3.8.3.1. Presentation and usage
        • 2.3.8.3.2. Advantages
        • 2.3.8.3.3. Drawbacks
        • 2.3.8.3.4. Mathematical formulation
      • 2.3.8.4. Homogeneity, completeness and V-measure
        • 2.3.8.4.1. Presentation and usage
        • 2.3.8.4.2. Advantages
        • 2.3.8.4.3. Drawbacks
        • 2.3.8.4.4. Mathematical formulation
      • 2.3.8.5. Silhouette Coefficient
        • 2.3.8.5.1. Presentation and usage
        • 2.3.8.5.2. Advantages
        • 2.3.8.5.3. Drawbacks
  • 2.4. Biclustering
    • 2.4.1. Spectral Co-Clustering
      • 2.4.1.1. Mathematical formulation
    • 2.4.2. Spectral Biclustering
      • 2.4.2.1. Mathematical formulation
    • 2.4.3. Biclustering evaluation
  • 2.5. Decomposing signals in components (matrix factorization problems)
    • 2.5.1. Principal component analysis (PCA)
      • 2.5.1.1. Exact PCA and probabilistic interpretation
      • 2.5.1.2. Approximate PCA
      • 2.5.1.3. Kernel PCA
      • 2.5.1.4. Sparse principal components analysis (SparsePCA and MiniBatchSparsePCA)
    • 2.5.2. Truncated singular value decomposition and latent semantic analysis
    • 2.5.3. Dictionary Learning
      • 2.5.3.1. Sparse coding with a precomputed dictionary
      • 2.5.3.2. Generic dictionary learning
      • 2.5.3.3. Mini-batch dictionary learning
    • 2.5.4. Factor Analysis
    • 2.5.5. Independent component analysis (ICA)
    • 2.5.6. Non-negative matrix factorization (NMF or NNMF)
  • 2.6. Covariance estimation
    • 2.6.1. Empirical covariance
    • 2.6.2. Shrunk Covariance
      • 2.6.2.1. Basic shrinkage
      • 2.6.2.2. Ledoit-Wolf shrinkage
      • 2.6.2.3. Oracle Approximating Shrinkage
    • 2.6.3. Sparse inverse covariance
    • 2.6.4. Robust Covariance Estimation
      • 2.6.4.1. Minimum Covariance Determinant
  • 2.7. Novelty and Outlier Detection
    • 2.7.1. Novelty Detection
    • 2.7.2. Outlier Detection
      • 2.7.2.1. Fitting an elliptic envelop
      • 2.7.2.2. One-class SVM versus elliptic envelop
  • 2.8. Hidden Markov Models
    • 2.8.1. Using HMM
      • 2.8.1.1. Building HMM and generating samples
      • 2.8.1.2. Training HMM parameters and inferring the hidden states
      • 2.8.1.3. Implementing HMMs with custom emission probabilities
  • 2.9. Density Estimation
    • 2.9.1. Density Estimation: Histograms
    • 2.9.2. Kernel Density Estimation
  • 2.10. Neural network models (unsupervised)
    • 2.10.1. Restricted Boltzmann machines
      • 2.10.1.1. Graphical model and parametrization
      • 2.10.1.2. Bernoulli Restricted Boltzmann machines
      • 2.10.1.3. Stochastic Maximum Likelihood learning
• 3. Model selection and evaluation
  • 3.1. Cross-validation: evaluating estimator performance
    • 3.1.1. Computing cross-validated metrics
    • 3.1.2. Cross validation iterators
      • 3.1.2.1. K-fold
      • 3.1.2.2. Stratified k-fold
      • 3.1.2.3. Leave-One-Out - LOO
      • 3.1.2.4. Leave-P-Out - LPO
      • 3.1.2.5. Leave-One-Label-Out - LOLO
      • 3.1.2.6. Leave-P-Label-Out
      • 3.1.2.7. Random permutations cross-validation a.k.a. Shuffle & Split
      • 3.1.2.8. See also
      • 3.1.2.9. Bootstrapping cross-validation
    • 3.1.3. Cross validation and model selection
  • 3.2. Grid Search: Searching for estimator parameters
    • 3.2.1. Exhaustive Grid Search
      • 3.2.1.1. Scoring functions for parameter search
    • 3.2.2. Randomized Parameter Optimization
    • 3.2.3. Alternatives to brute force parameter search
      • 3.2.3.1. Model specific cross-validation
        • 3.2.3.1.1. sklearn.linear_model.RidgeCV
        • 3.2.3.1.2. sklearn.linear_model.RidgeClassifierCV
        • 3.2.3.1.3. sklearn.linear_model.LarsCV
        • 3.2.3.1.4. sklearn.linear_model.LassoLarsCV
        • 3.2.3.1.5. sklearn.linear_model.LassoCV
        • 3.2.3.1.6. sklearn.linear_model.ElasticNetCV
      • 3.2.3.2. Information Criterion
        • 3.2.3.2.1. sklearn.linear_model.LassoLarsIC
      • 3.2.3.3. Out of Bag Estimates
        • 3.2.3.3.1. sklearn.ensemble.RandomForestClassifier
        • 3.2.3.3.2. sklearn.ensemble.RandomForestRegressor
        • 3.2.3.3.3. sklearn.ensemble.ExtraTreesClassifier
        • 3.2.3.3.4. sklearn.ensemble.ExtraTreesRegressor
        • 3.2.3.3.5. sklearn.ensemble.GradientBoostingClassifier
        • 3.2.3.3.6. sklearn.ensemble.GradientBoostingRegressor
  • 3.3. Pipeline: chaining estimators
    • 3.3.1. Usage
    • 3.3.2. Notes
  • 3.4. FeatureUnion: Combining feature extractors
    • 3.4.1. Usage
  • 3.5. Model evaluation: quantifying the quality of predictions
    • 3.5.1. The scoring parameter: defining model evaluation rules
      • 3.5.1.1. Common cases: predefined values
      • 3.5.1.2. Defining your scoring strategy from score functions
      • 3.5.1.3. Implementing your own scoring object
    • 3.5.2. Function for prediction-error metrics
      • 3.5.2.1. Classification metrics
        • 3.5.2.1.1. Accuracy score
        • 3.5.2.1.2. Confusion matrix
        • 3.5.2.1.3. Classification report
        • 3.5.2.1.4. Hamming loss
        • 3.5.2.1.5. Jaccard similarity coefficient score
        • 3.5.2.1.6. Precision, recall and F-measures
          • 3.5.2.1.6.1. Binary classification
          • 3.5.2.1.6.2. Multiclass and multilabel classification
        • 3.5.2.1.7. Hinge loss
        • 3.5.2.1.8. Log loss
        • 3.5.2.1.9. Matthews correlation coefficient
        • 3.5.2.1.10. Receiver operating characteristic (ROC)
        • 3.5.2.1.11. Zero one loss
      • 3.5.2.2. Regression metrics
        • 3.5.2.2.1. Explained variance score
        • 3.5.2.2.2. Mean absolute error
        • 3.5.2.2.3. Mean squared error
        • 3.5.2.2.4. R² score, the coefficient of determination
    • 3.5.3. Clustering metrics
    • 3.5.4. Biclustering metrics
      • 3.5.4.1. Clustering metrics
    • 3.5.5. Dummy estimators
• 4. Dataset transformations
  • 4.1. Feature extraction
    • 4.1.1. Loading features from dicts
    • 4.1.2. Feature hashing
      • 4.1.2.1. Implementation details
    • 4.1.3. Text feature extraction
      • 4.1.3.1. The Bag of Words representation
      • 4.1.3.2. Sparsity
      • 4.1.3.3. Common Vectorizer usage
      • 4.1.3.4. Tf–idf term weighting
      • 4.1.3.5. Decoding text files
      • 4.1.3.6. Applications and examples
      • 4.1.3.7. Limitations of the Bag of Words representation
      • 4.1.3.8. Vectorizing a large text corpus with the hashing trick
      • 4.1.3.9. Performing out-of-core scaling with HashingVectorizer
      • 4.1.3.10. Customizing the vectorizer classes
    • 4.1.4. Image feature extraction
      • 4.1.4.1. Patch extraction
      • 4.1.4.2. Connectivity graph of an image
  • 4.2. Preprocessing data
    • 4.2.1. Standardization, or mean removal and variance scaling
      • 4.2.1.1. Scaling features to a range
      • 4.2.1.2. Centering kernel matrices
    • 4.2.2. Normalization
    • 4.2.3. Binarization
      • 4.2.3.1. Feature binarization
    • 4.2.4. Encoding categorical features
    • 4.2.5. Label preprocessing
      • 4.2.5.1. Label binarization
      • 4.2.5.2. Label encoding
    • 4.2.6. Imputation of missing values
  • 4.3. Kernel Approximation
    • 4.3.1. Nystroem Method for Kernel Approximation
    • 4.3.2. Radial Basis Function Kernel
    • 4.3.3. Additive Chi Squared Kernel
    • 4.3.4. Skewed Chi Squared Kernel
    • 4.3.5. Mathematical Details
  • 4.4. Random Projection
    • 4.4.1. The Johnson-Lindenstrauss lemma
    • 4.4.2. Gaussian random projection
    • 4.4.3. Sparse random projection
  • 4.5. Pairwise metrics, Affinities and Kernels
    • 4.5.1. Cosine similarity
    • 4.5.2. Chi Squared Kernel
• 5. Dataset loading utilities
  • 5.1. General dataset API
  • 5.2. Toy datasets
  • 5.3. Sample images
  • 5.4. Sample generators
  • 5.5. Datasets in svmlight / libsvm format
  • 5.6. The Olivetti faces dataset
  • 5.7. The 20 newsgroups text dataset
    • 5.7.1. Usage
    • 5.7.2. Converting text to vectors
    • 5.7.3. Filtering text for more realistic training
  • 5.8. Downloading datasets from the mldata.org repository
  • 5.9. The Labeled Faces in the Wild face recognition dataset
    • 5.9.1. Usage
    • 5.9.2. Examples
  • 5.10. Forest covertypes
• 6. Scaling Strategies
  • 6.1. Scaling with instances using out-of-core learning
    • 6.1.1. Streaming instances
    • 6.1.2. Extracting features
    • 6.1.3. Incremental learning
    • 6.1.4. Examples
    • 6.1.5. Notes
• 7. Computational Performance
  • 7.1. Prediction Latency
    • 7.1.1. Bulk versus Atomic mode
    • 7.1.2. Influence of the Number of Features
    • 7.1.3. Influence of the Input Data Representation
    • 7.1.4. Influence of the Model Complexity
    • 7.1.5. Feature Extraction Latency
  • 7.2. Prediction Throughput
  • 7.3. Tips and Tricks
    • 7.3.1. Linear algebra libraries
    • 7.3.2. Model Compression
    • 7.3.3. Model Reshaping
    • 7.3.4. Links
• Examples
  • General examples
  • Examples based on real world datasets
  • Biclustering
  • Clustering
  • Covariance estimation
  • Cross decomposition
  • Dataset examples
  • Decomposition
  • Ensemble methods
  • Tutorial exercises
  • Gaussian Process for Machine Learning
  • Generalized Linear Models
  • Manifold learning
  • Gaussian Mixture Models
  • Nearest Neighbors
  • Semi Supervised Classification
  • Support Vector Machines
  • Decision Trees
• Support
  • Mailing List
  • User questions
  • Bug tracker
  • IRC
  • Documentation resources
• 0.15
  • Changelog
  • API changes summary
• 0.14
  • Changelog
  • API changes summary
  • People
• 0.13.1
  • Changelog
  • People
• 0.13
  • New Estimator Classes
  • Changelog
  • API changes summary
  • People
• 0.12.1
  • Changelog
  • People
• 0.12
  • Changelog
  • API changes summary
  • People
• 0.11
  • Changelog
    • Highlights
    • Other changes
  • API changes summary
  • People
• 0.10
  • Changelog
  • API changes summary
  • People
• 0.9
  • Changelog
  • API changes summary
  • People
• 0.8
  • Changelog
  • People
• 0.7
  • Changelog
  • People
• 0.6
  • Changelog
  • People
• 0.5
  • Changelog
  • New classes
  • Documentation
  • Fixes
  • Examples
  • External dependencies
  • Removed modules
  • Misc
  • Authors
• 0.4
  • Changelog
  • Authors
• Earlier versions
• External Resources, Videos and Talks
  • New to Scientific Python?
  • External Tutorials
  • Videos
• About us
  • History
  • People
  • Citing scikit-learn
  • Funding
    • Donating to the project
    • The 2013’ Paris international sprint
• Documentation of scikit-learn 0.15
• 5. Dataset loading utilities
  • 5.1. General dataset API
  • 5.2. Toy datasets
  • 5.3. Sample images
  • 5.4. Sample generators
  • 5.5. Datasets in svmlight / libsvm format
  • 5.6. The Olivetti faces dataset
  • 5.7. The 20 newsgroups text dataset
    • 5.7.1. Usage
    • 5.7.2. Converting text to vectors
    • 5.7.3. Filtering text for more realistic training
  • 5.8. Downloading datasets from the mldata.org repository
  • 5.9. The Labeled Faces in the Wild face recognition dataset
    • 5.9.1. Usage
    • 5.9.2. Examples
  • 5.10. Forest covertypes
• Forest covertypes
• The Labeled Faces in the Wild face recognition dataset
  • Usage
  • Examples
• Downloading datasets from the mldata.org repository
• The Olivetti faces dataset
• The 20 newsgroups text dataset
  • Usage
  • Converting text to vectors
  • Filtering text for more realistic training
• Reference
  • sklearn.base: Base classes and utility functions
    • Base classes
      • sklearn.base.BaseEstimator
      • sklearn.base.ClassifierMixin
      • sklearn.base.ClusterMixin
      • sklearn.base.RegressorMixin
      • sklearn.base.TransformerMixin
    • Functions
      • sklearn.base.clone
  • sklearn.cluster: Clustering
    • Classes
      • sklearn.cluster.AffinityPropagation
      • sklearn.cluster.DBSCAN
      • sklearn.cluster.KMeans
      • sklearn.cluster.MiniBatchKMeans
      • sklearn.cluster.MeanShift
      • sklearn.cluster.SpectralClustering
      • sklearn.cluster.Ward
    • Functions
      • sklearn.cluster.estimate_bandwidth
      • sklearn.cluster.k_means
      • sklearn.cluster.ward_tree
      • sklearn.cluster.affinity_propagation
      • sklearn.cluster.dbscan
      • sklearn.cluster.mean_shift
      • sklearn.cluster.spectral_clustering
  • sklearn.cluster.bicluster: Biclustering
    • Classes
      • sklearn.cluster.bicluster.SpectralBiclustering
      • sklearn.cluster.bicluster.SpectralCoclustering
  • sklearn.covariance: Covariance Estimators
    • sklearn.covariance.EmpiricalCovariance
    • sklearn.covariance.EllipticEnvelope
    • sklearn.covariance.GraphLasso
    • sklearn.covariance.GraphLassoCV
    • sklearn.covariance.LedoitWolf
    • sklearn.covariance.MinCovDet
    • sklearn.covariance.OAS
    • sklearn.covariance.ShrunkCovariance
    • sklearn.covariance.empirical_covariance
    • sklearn.covariance.ledoit_wolf
    • sklearn.covariance.shrunk_covariance
    • sklearn.covariance.oas
    • sklearn.covariance.graph_lasso
  • sklearn.cross_validation: Cross Validation
    • sklearn.cross_validation.Bootstrap
    • sklearn.cross_validation.KFold
    • sklearn.cross_validation.LeaveOneLabelOut
    • sklearn.cross_validation.LeaveOneOut
    • sklearn.cross_validation.LeavePLabelOut
    • sklearn.cross_validation.LeavePOut
    • sklearn.cross_validation.StratifiedKFold
    • sklearn.cross_validation.ShuffleSplit
    • sklearn.cross_validation.StratifiedShuffleSplit
    • sklearn.cross_validation.train_test_split
    • sklearn.cross_validation.cross_val_score
    • sklearn.cross_validation.permutation_test_score
    • sklearn.cross_validation.check_cv
  • sklearn.datasets: Datasets
    • Loaders
      • sklearn.datasets.fetch_20newsgroups
      • sklearn.datasets.fetch_20newsgroups_vectorized
      • sklearn.datasets.load_boston
      • sklearn.datasets.load_diabetes
      • sklearn.datasets.load_digits
      • sklearn.datasets.load_files
      • sklearn.datasets.load_iris
      • sklearn.datasets.load_lfw_pairs
      • sklearn.datasets.fetch_lfw_pairs
      • sklearn.datasets.load_lfw_people
      • sklearn.datasets.fetch_lfw_people
      • sklearn.datasets.load_linnerud
      • sklearn.datasets.fetch_mldata
      • sklearn.datasets.fetch_olivetti_faces
      • sklearn.datasets.fetch_california_housing
      • sklearn.datasets.fetch_covtype
      • sklearn.datasets.load_mlcomp
      • sklearn.datasets.load_sample_image
      • sklearn.datasets.load_sample_images
      • sklearn.datasets.load_svmlight_file
      • sklearn.datasets.dump_svmlight_file
    • Samples generator
      • sklearn.datasets.make_blobs
      • sklearn.datasets.make_classification
      • sklearn.datasets.make_circles
      • sklearn.datasets.make_friedman1
      • sklearn.datasets.make_friedman2
      • sklearn.datasets.make_friedman3
      • sklearn.datasets.make_gaussian_quantiles
      • sklearn.datasets.make_hastie_10_2
      • sklearn.datasets.make_low_rank_matrix
      • sklearn.datasets.make_moons
      • sklearn.datasets.make_multilabel_classification
      • sklearn.datasets.make_regression
      • sklearn.datasets.make_s_curve
      • sklearn.datasets.make_sparse_coded_signal
      • sklearn.datasets.make_sparse_spd_matrix
      • sklearn.datasets.make_sparse_uncorrelated
      • sklearn.datasets.make_spd_matrix
      • sklearn.datasets.make_swiss_roll
      • sklearn.datasets.make_biclusters
      • sklearn.datasets.make_checkerboard
  • sklearn.decomposition: Matrix Decomposition
    • sklearn.decomposition.PCA
    • sklearn.decomposition.ProjectedGradientNMF
    • sklearn.decomposition.RandomizedPCA
    • sklearn.decomposition.KernelPCA
    • sklearn.decomposition.FactorAnalysis
    • sklearn.decomposition.FastICA
    • sklearn.decomposition.TruncatedSVD
    • sklearn.decomposition.NMF
    • sklearn.decomposition.SparsePCA
    • sklearn.decomposition.MiniBatchSparsePCA
    • sklearn.decomposition.SparseCoder
    • sklearn.decomposition.DictionaryLearning
    • sklearn.decomposition.MiniBatchDictionaryLearning
    • sklearn.decomposition.fastica
    • sklearn.decomposition.dict_learning
    • sklearn.decomposition.dict_learning_online
    • sklearn.decomposition.sparse_encode
  • sklearn.dummy: Dummy estimators
    • sklearn.dummy.DummyClassifier
    • sklearn.dummy.DummyRegressor
  • sklearn.ensemble: Ensemble Methods
    • sklearn.ensemble.AdaBoostClassifier
    • sklearn.ensemble.AdaBoostRegressor
    • sklearn.ensemble.BaggingClassifier
    • sklearn.ensemble.BaggingRegressor
    • 3.2.3.3.3. sklearn.ensemble.ExtraTreesClassifier
    • 3.2.3.3.4. sklearn.ensemble.ExtraTreesRegressor
    • 3.2.3.3.5. sklearn.ensemble.GradientBoostingClassifier
    • 3.2.3.3.6. sklearn.ensemble.GradientBoostingRegressor
    • 3.2.3.3.1. sklearn.ensemble.RandomForestClassifier
    • sklearn.ensemble.RandomTreesEmbedding
    • 3.2.3.3.2. sklearn.ensemble.RandomForestRegressor
    • partial dependence
      • sklearn.ensemble.partial_dependence.partial_dependence
      • sklearn.ensemble.partial_dependence.plot_partial_dependence
  • sklearn.feature_extraction: Feature Extraction
    • sklearn.feature_extraction.DictVectorizer
    • sklearn.feature_extraction.FeatureHasher
    • From images
      • sklearn.feature_extraction.image.img_to_graph
      • sklearn.feature_extraction.image.grid_to_graph
      • sklearn.feature_extraction.image.extract_patches_2d
      • sklearn.feature_extraction.image.reconstruct_from_patches_2d
      • sklearn.feature_extraction.image.PatchExtractor
    • From text
      • sklearn.feature_extraction.text.CountVectorizer
      • sklearn.feature_extraction.text.HashingVectorizer
      • sklearn.feature_extraction.text.TfidfTransformer
      • sklearn.feature_extraction.text.TfidfVectorizer
  • sklearn.feature_selection: Feature Selection
    • sklearn.feature_selection.SelectPercentile
    • sklearn.feature_selection.SelectKBest
    • sklearn.feature_selection.SelectFpr
    • sklearn.feature_selection.SelectFdr
    • sklearn.feature_selection.SelectFwe
    • sklearn.feature_selection.RFE
    • sklearn.feature_selection.RFECV
    • sklearn.feature_selection.chi2
    • sklearn.feature_selection.f_classif
    • sklearn.feature_selection.f_regression
  • sklearn.gaussian_process: Gaussian Processes
    • sklearn.gaussian_process.GaussianProcess
    • sklearn.gaussian_process.correlation_models.absolute_exponential
    • sklearn.gaussian_process.correlation_models.squared_exponential
    • sklearn.gaussian_process.correlation_models.generalized_exponential
    • sklearn.gaussian_process.correlation_models.pure_nugget
    • sklearn.gaussian_process.correlation_models.cubic
    • sklearn.gaussian_process.correlation_models.linear
    • sklearn.gaussian_process.regression_models.constant
    • sklearn.gaussian_process.regression_models.linear
    • sklearn.gaussian_process.regression_models.quadratic
  • sklearn.grid_search: Grid Search
    • sklearn.grid_search.GridSearchCV
    • sklearn.grid_search.ParameterGrid
    • sklearn.grid_search.ParameterSampler
    • sklearn.grid_search.RandomizedSearchCV
  • sklearn.hmm: Hidden Markov Models
    • sklearn.hmm.GaussianHMM
    • sklearn.hmm.MultinomialHMM
    • sklearn.hmm.GMMHMM
  • sklearn.isotonic: Isotonic regression
    • sklearn.isotonic.IsotonicRegression
    • sklearn.isotonic.isotonic_regression
  • sklearn.kernel_approximation Kernel Approximation
    • sklearn.kernel_approximation.AdditiveChi2Sampler
    • sklearn.kernel_approximation.Nystroem
    • sklearn.kernel_approximation.RBFSampler
    • sklearn.kernel_approximation.SkewedChi2Sampler
  • sklearn.semi_supervised Semi-Supervised Learning
    • sklearn.semi_supervised.LabelPropagation
    • sklearn.semi_supervised.LabelSpreading
  • sklearn.lda: Linear Discriminant Analysis
    • sklearn.lda.LDA
  • sklearn.linear_model: Generalized Linear Models
    • sklearn.linear_model.ARDRegression
    • sklearn.linear_model.BayesianRidge
    • sklearn.linear_model.ElasticNet
    • 3.2.3.1.6. sklearn.linear_model.ElasticNetCV
    • sklearn.linear_model.Lars
    • 3.2.3.1.3. sklearn.linear_model.LarsCV
    • sklearn.linear_model.Lasso
    • 3.2.3.1.5. sklearn.linear_model.LassoCV
    • sklearn.linear_model.LassoLars
    • 3.2.3.1.4. sklearn.linear_model.LassoLarsCV
    • 3.2.3.2.1. sklearn.linear_model.LassoLarsIC
    • sklearn.linear_model.LinearRegression
    • sklearn.linear_model.LogisticRegression
    • sklearn.linear_model.MultiTaskLasso
    • sklearn.linear_model.MultiTaskElasticNet
    • sklearn.linear_model.OrthogonalMatchingPursuit
    • sklearn.linear_model.OrthogonalMatchingPursuitCV
    • sklearn.linear_model.PassiveAggressiveClassifier
    • sklearn.linear_model.PassiveAggressiveRegressor
    • sklearn.linear_model.Perceptron
    • sklearn.linear_model.RandomizedLasso
    • sklearn.linear_model.RandomizedLogisticRegression
    • sklearn.linear_model.Ridge
    • sklearn.linear_model.RidgeClassifier
    • 3.2.3.1.2. sklearn.linear_model.RidgeClassifierCV
    • 3.2.3.1.1. sklearn.linear_model.RidgeCV
    • sklearn.linear_model.SGDClassifier
    • sklearn.linear_model.SGDRegressor
    • sklearn.linear_model.lars_path
    • sklearn.linear_model.lasso_path
    • sklearn.linear_model.lasso_stability_path
    • sklearn.linear_model.orthogonal_mp
    • sklearn.linear_model.orthogonal_mp_gram
  • sklearn.manifold: Manifold Learning
    • sklearn.manifold.LocallyLinearEmbedding
    • sklearn.manifold.Isomap
    • sklearn.manifold.MDS
    • sklearn.manifold.SpectralEmbedding
    • sklearn.manifold.locally_linear_embedding
    • sklearn.manifold.spectral_embedding
  • sklearn.metrics: Metrics
    • Model Selection Interface
      • sklearn.metrics.make_scorer
    • Classification metrics
      • sklearn.metrics.accuracy_score
      • sklearn.metrics.auc
      • sklearn.metrics.average_precision_score
      • sklearn.metrics.classification_report
      • sklearn.metrics.confusion_matrix
      • sklearn.metrics.f1_score
      • sklearn.metrics.fbeta_score
      • sklearn.metrics.hamming_loss
      • sklearn.metrics.hinge_loss
      • sklearn.metrics.jaccard_similarity_score
      • sklearn.metrics.log_loss
      • sklearn.metrics.matthews_corrcoef
      • sklearn.metrics.precision_recall_curve
      • sklearn.metrics.precision_recall_fscore_support
      • sklearn.metrics.precision_score
      • sklearn.metrics.recall_score
      • sklearn.metrics.roc_auc_score
      • sklearn.metrics.roc_curve
      • sklearn.metrics.zero_one_loss
    • Regression metrics
      • sklearn.metrics.explained_variance_score
      • sklearn.metrics.mean_absolute_error
      • sklearn.metrics.mean_squared_error
      • sklearn.metrics.r2_score
    • Clustering metrics
      • sklearn.metrics.adjusted_mutual_info_score
      • sklearn.metrics.adjusted_rand_score
      • sklearn.metrics.completeness_score
      • sklearn.metrics.homogeneity_completeness_v_measure
      • sklearn.metrics.homogeneity_score
      • sklearn.metrics.mutual_info_score
      • sklearn.metrics.normalized_mutual_info_score
      • sklearn.metrics.silhouette_score
      • sklearn.metrics.silhouette_samples
      • sklearn.metrics.v_measure_score
    • Biclustering metrics
      • sklearn.metrics.consensus_score
    • Pairwise metrics
      • sklearn.metrics.pairwise.additive_chi2_kernel
      • sklearn.metrics.pairwise.chi2_kernel
      • sklearn.metrics.pairwise.distance_metrics
      • sklearn.metrics.pairwise.euclidean_distances
      • sklearn.metrics.pairwise.kernel_metrics
      • sklearn.metrics.pairwise.linear_kernel
      • sklearn.metrics.pairwise.manhattan_distances
      • sklearn.metrics.pairwise.pairwise_distances
      • sklearn.metrics.pairwise.pairwise_kernels
      • sklearn.metrics.pairwise.polynomial_kernel
      • sklearn.metrics.pairwise.rbf_kernel
  • sklearn.mixture: Gaussian Mixture Models
    • sklearn.mixture.GMM
    • sklearn.mixture.DPGMM
    • sklearn.mixture.VBGMM
  • sklearn.multiclass: Multiclass and multilabel classification
    • Multiclass and multilabel classification strategies
    • sklearn.multiclass.OneVsRestClassifier
    • sklearn.multiclass.OneVsOneClassifier
    • sklearn.multiclass.OutputCodeClassifier
    • sklearn.multiclass.fit_ovr
    • sklearn.multiclass.predict_ovr
    • sklearn.multiclass.fit_ovo
    • sklearn.multiclass.predict_ovo
    • sklearn.multiclass.fit_ecoc
    • sklearn.multiclass.predict_ecoc
  • sklearn.naive_bayes: Naive Bayes
    • sklearn.naive_bayes.GaussianNB
    • sklearn.naive_bayes.MultinomialNB
    • sklearn.naive_bayes.BernoulliNB
  • sklearn.neighbors: Nearest Neighbors
    • sklearn.neighbors.NearestNeighbors
    • sklearn.neighbors.KNeighborsClassifier
    • sklearn.neighbors.RadiusNeighborsClassifier
    • sklearn.neighbors.KNeighborsRegressor
    • sklearn.neighbors.RadiusNeighborsRegressor
    • sklearn.neighbors.NearestCentroid
    • sklearn.neighbors.BallTree
    • sklearn.neighbors.KDTree
    • sklearn.neighbors.DistanceMetric
    • sklearn.neighbors.KernelDensity
    • sklearn.neighbors.kneighbors_graph
    • sklearn.neighbors.radius_neighbors_graph
  • sklearn.neural_network: Neural network models
    • sklearn.neural_network.BernoulliRBM
  • sklearn.cross_decomposition: Cross decomposition
    • sklearn.cross_decomposition.PLSRegression
    • sklearn.cross_decomposition.PLSCanonical
    • sklearn.cross_decomposition.CCA
    • sklearn.cross_decomposition.PLSSVD
  • sklearn.pipeline: Pipeline
    • sklearn.pipeline.Pipeline
    • sklearn.pipeline.FeatureUnion
    • sklearn.pipeline.make_pipeline
    • sklearn.pipeline.make_union
  • sklearn.preprocessing: Preprocessing and Normalization
    • sklearn.preprocessing.Binarizer
    • sklearn.preprocessing.Imputer
    • sklearn.preprocessing.KernelCenterer
    • sklearn.preprocessing.LabelBinarizer
    • sklearn.preprocessing.LabelEncoder
    • sklearn.preprocessing.MinMaxScaler
    • sklearn.preprocessing.Normalizer
    • sklearn.preprocessing.OneHotEncoder
    • sklearn.preprocessing.StandardScaler
    • sklearn.preprocessing.add_dummy_feature
    • sklearn.preprocessing.binarize
    • sklearn.preprocessing.label_binarize
    • sklearn.preprocessing.normalize
    • sklearn.preprocessing.scale
  • sklearn.qda: Quadratic Discriminant Analysis
    • sklearn.qda.QDA
  • sklearn.random_projection: Random projection
    • sklearn.random_projection.GaussianRandomProjection
    • sklearn.random_projection.SparseRandomProjection
    • sklearn.random_projection.johnson_lindenstrauss_min_dim
  • sklearn.svm: Support Vector Machines
    • Estimators
      • sklearn.svm.SVC
      • sklearn.svm.LinearSVC
      • sklearn.svm.NuSVC
      • sklearn.svm.SVR
      • sklearn.svm.NuSVR
      • sklearn.svm.OneClassSVM
      • sklearn.svm.l1_min_c
    • Low-level methods
      • sklearn.svm.libsvm.fit
      • sklearn.svm.libsvm.decision_function
      • sklearn.svm.libsvm.predict
      • sklearn.svm.libsvm.predict_proba
      • sklearn.svm.libsvm.cross_validation
  • sklearn.tree: Decision Trees
    • sklearn.tree.DecisionTreeClassifier
    • sklearn.tree.DecisionTreeRegressor
    • sklearn.tree.ExtraTreeClassifier
    • sklearn.tree.ExtraTreeRegressor
    • sklearn.tree.export_graphviz
  • sklearn.utils: Utilities
    • sklearn.utils.check_random_state
    • sklearn.utils.resample
    • sklearn.utils.shuffle
• Who is using scikit-learn?
  • Spotify
  • Inria
  • Evernote
  • Télécom ParisTech
  • AWeber
  • Yhat
  • Rangespan
  • Birchbox
  • Bestofmedia Group
  • Change.org
  • PHIMECA Engineering
• Contributing
  • Submitting a bug report
  • Retrieving the latest code
  • Contributing code
    • How to contribute
    • Easy Issues
    • Documentation
    • Testing and improving test coverage
    • Developers web site
    • Issue Tracker Tags
  • Other ways to contribute
  • Coding guidelines
    • Input validation
    • Random Numbers
    • Deprecation
    • Python 3.x support
  • APIs of scikit-learn objects
    • Different objects
    • Estimators
      • Instantiation
      • Fitting
      • Estimated Attributes
      • Optional Arguments
  • Rolling your own estimator
    • get_params and set_params
    • Parameters and init
    • Cloning
    • Pipeline compatibility
    • Working notes
    • Specific models
• Developers’ Tips for Debugging
  • Memory errors: debugging Cython with valgrind
• Maintainer / core-developer information
  • Making a release
• How to optimize for speed
  • Python, Cython or C/C++?
  • Fast matrix multiplications
  • Profiling Python code
  • Memory usage profiling
  • Performance tips for the Cython developer
  • Profiling compiled extensions
    • Using yep and google-perftools
    • Using gprof
    • Using valgrind / callgrind / kcachegrind
  • Multi-core parallelism using joblib.Parallel
  • A sample algorithmic trick: warm restarts for cross validation
• Utilities for Developers
  • Validation Tools
  • Efficient Linear Algebra & Array Operations
  • Efficient Random Sampling
  • Efficient Routines for Sparse Matrices
  • Graph Routines
  • Backports
    • ARPACK
    • Benchmarking
  • Testing Functions
  • Multiclass and multilabel utility function
  • Helper Functions
  • Hash Functions
  • Warnings and Exceptions
• Installing scikit-learn
  • Installing an official release
    • Getting the dependencies
      • Easy install
      • From source package
    • Windows installer
    • Building on windows
  • Third party distributions of scikit-learn
    • Debian and derivatives (Ubuntu)
    • Python(x,y)
    • Enthought Python distribution
    • MacPorts
    • Archlinux
    • NetBSD
  • Bleeding Edge
  • Testing
• An introduction to machine learning with scikit-learn
  • Machine learning: the problem setting
  • Loading an example dataset
  • Learning and predicting
  • Model persistence
• Choosing the right estimator

Classification

Identifying to which set of categories a new observation belong to.

Applications: Spam detection, Image recognition.
Algorithms: 

SVM, nearest neighbors, random forest, ...

Examples

Regression

Predicting a continuous value for a new example.

Applications: Drug response, Stock prices.
Algorithms: 

SVR, ridge regression, Lasso, ...

Examples

Clustering

Automatic grouping of similar objects into sets.

Applications: Customer segmentation, Grouping experiment outcomes
Algorithms: 

k-Means, spectral clustering, mean-shift, ...

Examples

Dimensionality reduction

Reducing the number of random variables to consider.

Applications: Visualization, Increased efficiency
Algorithms: 

PCA, Isomap, non-negative matrix factorization.

Examples

Model selection

Comparing, validating and choosing parameters and models.

Goal: Improved accuracy via parameter tuning
Modules: 

grid search, cross validation, metrics.

Examples

Preprocessing

Feature extraction and normalization.

Application: Transforming input data such as text for use with machine learning algorithms.
Modules: 

preprocessing, feature extraction.

Examples

News

• On-going development: What's new (changelog)
• August 2013. scikit-learn 0.14 is available for download (Changelog).
• July 22-28th, 2013: international sprint. During this week-long sprint, we gathered most of the core developers in Paris. We want to thank our sponsors, our hosts Télécom ParisTech and tinyclues, and donations that helped fund this event.

Community

• About us See authors # scikit-learn
• Questions? See stackoverflow # scikit-learn
• Mailing list: scikit-learn-general@lists.sourceforge.net
• IRC: #scikit-learn @ freenode

Help us, donate! Cite us! Read more about donations

Who uses scikit-learn?

"We use scikit-learn to support leading-edge basic research [...]"

"I think it's the most well-designed ML package I've seen so far."

"scikit-learn's ease-of-use, performance and overall variety of algorithms implemented has proved invaluable [...]."

"For these tasks, we relied on the excellent scikit-learn package for Python."

"The great benefit of scikit-learn is its fast learning curve [...]"

"It allows us to do AWesome stuff we would not otherwise accomplish"

"scikit-learn makes doing advanced analysis in Python accessible to anyone."

More testimonials

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