• 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 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
      • Exercises
    • 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. Fitting additional weak-learners
      • 1.9.4.4. Controlling the tree size
      • 1.9.4.5. Mathematical formulation
        • 1.9.4.5.1. Loss Functions
      • 1.9.4.6. Regularization
        • 1.9.4.6.1. Shrinkage
        • 1.9.4.6.2. Subsampling
      • 1.9.4.7. Interpretation
        • 1.9.4.7.1. Feature importance
        • 1.9.4.7.2. 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: Dirichlet 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. t-distributed Stochastic Neighbor Embedding (t-SNE)
    • 2.2.10. 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. Different linkage type: Ward, complete and average linkage
      • 2.3.6.2. Adding connectivity constraints
      • 2.3.6.3. Varying the metric
    • 2.3.7. DBSCAN
    • 2.3.8. Clustering performance evaluation
      • 2.3.8.1. Adjusted Rand index
        • 2.3.8.1.1. Presentation and usage
        • 2.3.8.1.2. Advantages
        • 2.3.8.1.3. Drawbacks
        • 2.3.8.1.4. Mathematical formulation
      • 2.3.8.2. Mutual Information based scores
        • 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. Homogeneity, completeness and V-measure
        • 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. Silhouette Coefficient
        • 2.3.8.4.1. Presentation and usage
        • 2.3.8.4.2. Advantages
        • 2.3.8.4.3. 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 envelope
      • 2.7.2.2. One-class SVM versus elliptic envelope
  • 2.8. Density Estimation
    • 2.8.1. Density Estimation: Histograms
    • 2.8.2. Kernel Density Estimation
  • 2.9. Neural network models (unsupervised)
    • 2.9.1. Restricted Boltzmann machines
      • 2.9.1.1. Graphical model and parametrization
      • 2.9.1.2. Bernoulli Restricted Boltzmann machines
      • 2.9.1.3. 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
    • 3.1.3. A note on shuffling
    • 3.1.4. 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
  • 3.6. Model persistence
    • 3.6.1. Persistence example
    • 3.6.2. Security & maintainability limitations
  • 3.7. Validation curves: plotting scores to evaluate models
    • 3.7.1. Validation curve
    • 3.7.2. Learning curve
• 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
    • 4.2.7. Unsupervised data reduction
      • 4.2.7.1. PCA: principal component analysis
      • 4.2.7.2. Random projections
      • 4.2.7.3. Feature agglometration
  • 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. Strategies to scale computationally: bigger data
  • 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
• Frequently Asked Questions
  • What is the project name (a lot of people get it wrong)?
  • How do you pronounce the project name?
  • Why scikit?
  • How can I contribute to scikit-learn?
  • Can I add this new algorithm that I (or someone else) just published?
  • Can I add this classical algorithm from the 80s?
  • Why did you remove HMMs from scikit-learn?
  • Will you add graphical models or sequence prediction to scikit-learn?
  • Will you add GPU support?
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• Support
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• 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.AgglomerativeClustering
      • sklearn.cluster.DBSCAN
      • sklearn.cluster.FeatureAgglomeration
      • 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.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.GenericUnivariateSelect
    • 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.VarianceThreshold
    • 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.isotonic: Isotonic regression
    • sklearn.isotonic.IsotonicRegression
    • sklearn.isotonic.isotonic_regression
    • sklearn.isotonic.check_increasing
  • sklearn.kernel_approximation Kernel Approximation
    • sklearn.kernel_approximation.AdditiveChi2Sampler
    • sklearn.kernel_approximation.Nystroem
    • sklearn.kernel_approximation.RBFSampler
    • sklearn.kernel_approximation.SkewedChi2Sampler
  • sklearn.lda: Linear Discriminant Analysis
    • sklearn.lda.LDA
  • sklearn.learning_curve Learning curve evaluation
    • sklearn.learning_curve.learning_curve
    • sklearn.learning_curve.validation_curve
  • 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.MultiTaskLassoCV
    • sklearn.linear_model.MultiTaskElasticNetCV
    • 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.RANSACRegressor
    • 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.TSNE
    • 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.metrics.pairwise_distances
      • sklearn.metrics.pairwise_distances_argmin
      • sklearn.metrics.pairwise_distances_argmin_min
  • 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.MultiLabelBinarizer
    • sklearn.preprocessing.MinMaxScaler
    • sklearn.preprocessing.Normalizer
    • sklearn.preprocessing.OneHotEncoder
    • sklearn.preprocessing.StandardScaler
    • sklearn.preprocessing.PolynomialFeatures
    • 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.semi_supervised Semi-Supervised Learning
    • sklearn.semi_supervised.LabelPropagation
    • sklearn.semi_supervised.LabelSpreading
  • 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
  • HowAboutWe
  • PeerIndex
  • DataRobot
  • OkCupid
  • Lovely
  • Data Publica
• 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
    • Windows
    • Mac OSX
    • Linux
      • Installing build dependencies
      • Building scikit-learn with pip
      • From source package
  • Third party distributions of scikit-learn
    • Debian and derivatives (Ubuntu)
    • Python(x,y) for Windows
    • Canopy and Anaconda for all supported platforms
    • MacPorts for Mac OSX
    • Arch Linux
    • NetBSD
    • Fedora
  • Building on windows
    • 32-bit Python
    • 64-bit Python
    • Building binary packages and installers
    • Using an alternative compiler
  • Bleeding Edge
  • Testing
    • Testing scikit-learn once installed
    • Testing scikit-learn from within the source folder
• 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, feature selection, 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 2014. scikit-learn 0.15.1 is available for download (Changelog).
• July 2014. scikit-learn 0.15.0 is available for download (Changelog).
• July 14-20th, 2014: international sprint. During this week-long sprint, we gathered 18 of the core contributors in Paris. We want to thank our sponsors: Paris-Saclay Center for Data Science & Digicosme and our hosts La Paillasse, Criteo, Inria, and tinyclues.
• August 2013. scikit-learn 0.14 is available for download (Changelog).

Community

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

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"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"

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