User guide: contents

• 1. Installing scikit-learn
  • 1.1. Installing an official release
    • 1.1.1. Installing from source
      • 1.1.1.1. Easy install
      • 1.1.1.2. From source package
    • 1.1.2. Windows installer
    • 1.1.3. Building on windows
  • 1.2. Third party distributions of scikit-learn
    • 1.2.1. Debian and derivatives (Ubuntu)
    • 1.2.2. Python(x, y)
    • 1.2.3. Enthought Python distribution
    • 1.2.4. Macports
    • 1.2.5. NetBSD
  • 1.3. Bleeding Edge
  • 1.4. Testing
• 2. Tutorials: From the bottom up with scikit-learn
  • 2.1. An Introduction to machine learning with scikit-learn
    • 2.1.1. Machine learning: the problem setting
    • 2.1.2. Loading an example dataset
    • 2.1.3. Learning and Predicting
    • 2.1.4. Model persistence
  • 2.2. Scikit-learn tutorial: statistical-learning for sientific data processing
    • 1. Statistical learning: the setting and the estimator object in the scikit-learn
      • 1.1. Datasets
      • 1.2. Estimators objects
    • 2. Supervised learning: predicting an output variable from high-dimensional observations
      • 2.1. Nearest neighbor and the curse of dimensionality
        • 2.1.1. k-Nearest neighbors classifier
        • 2.1.2. The curse of dimensionality
      • 2.2. Linear model: from regression to sparsity
        • 2.2.1. Linear regression
        • 2.2.2. Shrinkage
        • 2.2.3. Sparsity
        • 2.2.4. Classification
          • 2.2.4.1. Excercice: classification of digits
      • 2.3. Support vector machines (SVMs)
        • 2.3.1. Linear SVMs
        • 2.3.2. Using kernels
          • 2.3.2.1. Excercice: classification of iris
    • 3. Model selection: choosing estimators and their parameters
      • 3.1. Score, and cross-validated scores
      • 3.2. Cross-validation generators
        • 3.2.1. Excercice: model selection on digits
      • 3.3. Grid-search and cross-validated estimators
        • 3.3.1. Grid-search
        • 3.3.2. Cross-validated estimators
          • 3.3.2.1. Excercice: setting sparsity on diabetes
    • 4. Unsupervised learning: seeking representations of the data
      • 4.1. Clustering: grouping observations together
        • 4.1.1. K-means clustering
        • 4.1.2. Hierarchical clustering: Ward
          • 4.1.2.1. Connectivity-constrained clustering
          • 4.1.2.2. Feature agglomeration
      • 4.2. Decompositions: from a signal to components and loadings
        • 4.2.1. Principal component analysis: PCA
        • 4.2.2. Indenpendant Component Analysis: ICA
    • 5. Putting it all together
      • 5.1. Pipelining
      • 5.2. Face recognition with eigenfaces
      • 5.3. Open problem: stock market structure
    • 6. Finding help
      • 6.1. The project mailing list
      • 6.2. Q&A communities with Machine Learning practictioners
  • 2.3. Using scikit-learn with Astronomical Data
    • 2.3.1. Astronomy Tutorial setup
      • 2.3.1.1. Setup
        • 2.3.1.1.1. Install scikit-learn build dependencies
        • 2.3.1.1.2. Build scikit-learn from source
        • 2.3.1.1.3. Download the datasets
    • 2.3.2. Machine Learning 101: General Concepts
      • 2.3.2.1. Features and feature extraction
        • 2.3.2.1.1. A simple example: the iris dataset
        • 2.3.2.1.2. Handling categorical features
        • 2.3.2.1.3. Extracting features from unstructured data
      • 2.3.2.2. Supervised Learning: model.fit(X, y)
        • 2.3.2.2.1. Classification
          • 2.3.2.2.1.1. A first classifier example with scikit-learn
          • 2.3.2.2.1.2. Notable implementations of classifiers
          • 2.3.2.2.1.3. Sample application of classifiers
        • 2.3.2.2.2. Regression
      • 2.3.2.3. Unsupervised Learning: model.fit(X)
        • 2.3.2.3.1. Dimensionality Reduction and visualization
          • 2.3.2.3.1.1. Normalization and visualization with PCA
          • 2.3.2.3.1.2. Other applications of dimensionality reduction
        • 2.3.2.3.2. Clustering
          • 2.3.2.3.2.1. Notable implementations of clustering models
          • 2.3.2.3.2.2. Applications of clustering
      • 2.3.2.4. Linearly separable data
      • 2.3.2.5. Training set, test set and overfitting
        • 2.3.2.5.1. The overfitting issue
        • 2.3.2.5.2. Solutions to overfitting
        • 2.3.2.5.3. Measuring classification performance on a test set
      • 2.3.2.6. Key takeaway points
    • 2.3.3. Machine Learning 102: Practical Advice
      • 2.3.3.1. Bias, Variance, Over-fitting, and Under-fitting
      • 2.3.3.2. Cross-Validation and Testing
      • 2.3.3.3. Learning Curves
      • 2.3.3.4. Summary
        • 2.3.3.4.1. High Bias
        • 2.3.3.4.2. High Variance
    • 2.3.4. Classification: Learning Labels of Astronomical Sources
      • 2.3.4.1. Motivation: Why is this Important?
      • 2.3.4.2. Star-Quasar Classification: Naive Bayes
    • 2.3.5. Regression: Photometric Redshifts of Galaxies
      • 2.3.5.1. Motivation: Dark Energy, Dark Matter, and the Fate of the Universe
      • 2.3.5.2. A Simple Method: Decision Tree Regression
    • 2.3.6. Dimensionality Reduction of Astronomical Spectra
      • 2.3.6.1. SDSS Spectral Data
      • 2.3.6.2. Principal Component Analysis
      • 2.3.6.3. References
    • 2.3.7. Exercises: Taking it a step further
      • 2.3.7.1. Exercise 1: Photometric Classification with GMM
      • 2.3.7.2. Exercise 2: Photometric redshifts with Decision Trees
      • 2.3.7.3. Exercise 3: Dimensionality Reduction of Spectra
        • 2.3.7.3.1. Programming
        • 2.3.7.3.2. Experimentation
  • 2.4. Machine Learning for Text Analytics with scikit-learn
    • 2.4.1. Tutorial setup
      • 2.4.1.1. Install scikit-learn build dependencies
      • 2.4.1.2. Build scikit-learn from source
      • 2.4.1.3. Download the datasets
    • 2.4.2. Machine Learning 101: General Concepts
      • 2.4.2.1. Features and feature extraction
        • 2.4.2.1.1. A simple example: the iris dataset
        • 2.4.2.1.2. Handling categorical features
        • 2.4.2.1.3. Extracting features from unstructured data
      • 2.4.2.2. Supervised Learning: model.fit(X, y)
        • 2.4.2.2.1. Classification
          • 2.4.2.2.1.1. A first classifier example with scikit-learn
          • 2.4.2.2.1.2. Notable implementations of classifiers
          • 2.4.2.2.1.3. Sample application of classifiers
        • 2.4.2.2.2. Regression
      • 2.4.2.3. Unsupervised Learning: model.fit(X)
        • 2.4.2.3.1. Dimensionality Reduction and visualization
          • 2.4.2.3.1.1. Normalization and visualization with PCA
          • 2.4.2.3.1.2. Other applications of dimensionality reduction
        • 2.4.2.3.2. Clustering
          • 2.4.2.3.2.1. Notable implementations of clustering models
          • 2.4.2.3.2.2. Applications of clustering
      • 2.4.2.4. Linearly separable data
      • 2.4.2.5. Training set, test set and overfitting
        • 2.4.2.5.1. The overfitting issue
        • 2.4.2.5.2. Solutions to overfitting
        • 2.4.2.5.3. Measuring classification performance on a test set
        • 2.4.2.5.4. Measuring performance using cross-validation
      • 2.4.2.6. Key takeaway points
    • 2.4.3. Working with text data
      • 2.4.3.1. Downloading the data and loading it from Python
      • 2.4.3.2. Extracting features from text files
        • 2.4.3.2.1. Bags of words
        • 2.4.3.2.2. Tokenizing text with scikit-learn
        • 2.4.3.2.3. From occurrence counts to normalized frequencies
      • 2.4.3.3. Training a classifier
      • 2.4.3.4. Building a pipeline
      • 2.4.3.5. Evaluation of the performance on the test set
      • 2.4.3.6. Parameter tuning using grid search
    • 2.4.4. Exercises
      • 2.4.4.1. Exercise 0: Digits recognition
      • 2.4.4.2. Exercise 1: Language identification
      • 2.4.4.3. Exercise 2: Sentiment Analysis on movie reviews
      • 2.4.4.4. Exercise 3: Unsupervised topic extraction
      • 2.4.4.5. Exercise 4: CLI text classification utility
    • 2.4.5. How to select the right algorithm for the task
    • 2.4.6. Finding help
      • 2.4.6.1. The project mailing list
      • 2.4.6.2. Q&A communities with Machine Learning practictioners
• 3. Supervised learning
  • 3.1. Generalized Linear Models
    • 3.1.1. Ordinary Least Squares
      • 3.1.1.1. Ordinary Least Squares Complexity
    • 3.1.2. Ridge Regression
      • 3.1.2.1. Ridge Complexity
      • 3.1.2.2. Setting the regularization parameter: generalized Cross-Validation
    • 3.1.3. Lasso
      • 3.1.3.1. Setting regularization parameter
        • 3.1.3.1.1. Using cross-validation
        • 3.1.3.1.2. Information-criteria based model selection
    • 3.1.4. Elastic Net
    • 3.1.5. Least Angle Regression
    • 3.1.6. LARS Lasso
      • 3.1.6.1. Mathematical formulation
    • 3.1.7. Orthogonal Matching Pursuit (OMP)
    • 3.1.8. Bayesian Regression
      • 3.1.8.1. Bayesian Ridge Regression
      • 3.1.8.2. Automatic Relevance Determination - ARD
    • 3.1.9. Logisitic regression
    • 3.1.10. Stochastic Gradient Descent - SGD
    • 3.1.11. Perceptron
  • 3.2. Support Vector Machines
    • 3.2.1. Classification
      • 3.2.1.1. Multi-class classification
      • 3.2.1.2. Unbalanced problems
    • 3.2.2. Regression
    • 3.2.3. Density estimation, novelty detection
    • 3.2.4. Complexity
    • 3.2.5. Tips on Practical Use
    • 3.2.6. Kernel functions
      • 3.2.6.1. Custom Kernels
        • 3.2.6.1.1. Using python functions as kernels
        • 3.2.6.1.2. Using the Gram matrix
    • 3.2.7. Mathematical formulation
      • 3.2.7.1. SVC
      • 3.2.7.2. NuSVC
    • 3.2.8. Implementation details
  • 3.3. Stochastic Gradient Descent
    • 3.3.1. Classification
    • 3.3.2. Regression
    • 3.3.3. Stochastic Gradient Descent for sparse data
    • 3.3.4. Complexity
    • 3.3.5. Tips on Practical Use
    • 3.3.6. Mathematical formulation
      • 3.3.6.1. SGD
    • 3.3.7. Implementation details
  • 3.4. Nearest Neighbors
    • 3.4.1. Unsupervised Nearest Neighbors
    • 3.4.2. Nearest Neighbors Classification
    • 3.4.3. Nearest Neighbors Regression
    • 3.4.4. Nearest Neighbor Algorithms
      • 3.4.4.1. Brute Force
      • 3.4.4.2. K-D Tree
      • 3.4.4.3. Ball Tree
      • 3.4.4.4. Choice of Nearest Neighbors Algorithm
      • 3.4.4.5. Effect of leaf_size
  • 3.5. Gaussian Processes
    • 3.5.1. Examples
      • 3.5.1.1. An introductory regression example
      • 3.5.1.2. Fitting Noisy Data
    • 3.5.2. Mathematical formulation
      • 3.5.2.1. The initial assumption
      • 3.5.2.2. The best linear unbiased prediction (BLUP)
      • 3.5.2.3. The empirical best linear unbiased predictor (EBLUP)
    • 3.5.3. Correlation Models
    • 3.5.4. Regression Models
    • 3.5.5. Implementation details
  • 3.6. Partial Least Squares
  • 3.7. Naive Bayes
    • 3.7.1. Gaussian Naive Bayes
    • 3.7.2. Multinomial Naive Bayes
    • 3.7.3. Bernoulli Naive Bayes
  • 3.8. Decision Trees
    • 3.8.1. Classification
    • 3.8.2. Regression
    • 3.8.3. Complexity
    • 3.8.4. Tips on practical use
    • 3.8.5. Tree algorithms: ID3, C4.5, C5.0 and CART
    • 3.8.6. Mathematical formulation
      • 3.8.6.1. Classification criteria
      • 3.8.6.2. Regression criteria
  • 3.9. Ensemble methods
    • 3.9.1. Forests of randomized trees
      • 3.9.1.1. Random Forests
      • 3.9.1.2. Extremely Randomized Trees
      • 3.9.1.3. Parameters
      • 3.9.1.4. Parallelization
  • 3.10. Multiclass and multilabel algorithms
    • 3.10.1. One-Vs-The-Rest
      • 3.10.1.1. Multilabel learning with OvR
    • 3.10.2. One-Vs-One
    • 3.10.3. Error-Correcting Output-Codes
  • 3.11. Feature selection
    • 3.11.1. Univariate feature selection
    • 3.11.2. Recursive feature elimination
    • 3.11.3. L1-based feature selection
      • 3.11.3.1. Selecting non-zero coefficients
      • 3.11.3.2. Randomized sparse models
    • 3.11.4. Tree-based feature selection
  • 3.12. Semi-Supervised
    • 3.12.1. Label Propagation
  • 3.13. Linear and Quadratic Discriminant Analysis
    • 3.13.1. Dimensionality Reduction using LDA
    • 3.13.2. Mathematical Idea
• 4. Unsupervised learning
  • 4.1. Gaussian mixture models
    • 4.1.1. GMM classifier
      • 4.1.1.1. Pros and cons of class GMM: expectation-maximization inference
        • 4.1.1.1.1. Pros
        • 4.1.1.1.2. Cons
      • 4.1.1.2. Selecting the number of components in a classical GMM
      • 4.1.1.3. Estimation algorithm Expectation-maximization
    • 4.1.2. VBGMM classifier: variational Gaussian mixtures
      • 4.1.2.1. Pros and cons of class VBGMM: variational inference
        • 4.1.2.1.1. Pros
        • 4.1.2.1.2. Cons
      • 4.1.2.2. Estimation algorithm: variational inference
    • 4.1.3. DPGMM classifier: Infinite Gaussian mixtures
      • 4.1.3.1. Pros and cons of class DPGMM: Diriclet process mixture model
        • 4.1.3.1.1. Pros
        • 4.1.3.1.2. Cons
      • 4.1.3.2. The Dirichlet Process
  • 4.2. Manifold learning
    • 4.2.1. Introduction
    • 4.2.2. Isomap
      • 4.2.2.1. Complexity
    • 4.2.3. Locally Linear Embedding
      • 4.2.3.1. Complexity
    • 4.2.4. Modified Locally Linear Embedding
      • 4.2.4.1. Complexity
    • 4.2.5. Hessian Eigenmapping
      • 4.2.5.1. Complexity
    • 4.2.6. Local Tangent Space Alignment
      • 4.2.6.1. Complexity
    • 4.2.7. Tips on practical use
  • 4.3. Clustering
    • 4.3.1. K-means
      • 4.3.1.1. Mini Batch K-Means
    • 4.3.2. Affinity propagation
    • 4.3.3. Mean Shift
    • 4.3.4. Spectral clustering
    • 4.3.5. Hierarchical clustering
      • 4.3.5.1. Adding connectivity constraints
    • 4.3.6. DBSCAN
    • 4.3.7. Clustering performance evaluation
      • 4.3.7.1. Inertia
        • 4.3.7.1.1. Presentation and usage
        • 4.3.7.1.2. Advantages
        • 4.3.7.1.3. Drawbacks
      • 4.3.7.2. Adjusted Rand index
        • 4.3.7.2.1. Presentation and usage
        • 4.3.7.2.2. Advantages
        • 4.3.7.2.3. Drawbacks
        • 4.3.7.2.4. Mathematical formulation
      • 4.3.7.3. Adjusted Mutual Information
        • 4.3.7.3.1. Presentation and usage
        • 4.3.7.3.2. Advantages
        • 4.3.7.3.3. Drawbacks
        • 4.3.7.3.4. Mathematical formulation
      • 4.3.7.4. Homogeneity, completeness and V-measure
        • 4.3.7.4.1. Presentation and usage
        • 4.3.7.4.2. Advantages
        • 4.3.7.4.3. Drawbacks
        • 4.3.7.4.4. Mathematical formulation
      • 4.3.7.5. Silhouette Coefficient
        • 4.3.7.5.1. Presentation and usage
        • 4.3.7.5.2. Advantages
        • 4.3.7.5.3. Drawbacks
  • 4.4. Decomposing signals in components (matrix factorization problems)
    • 4.4.1. Principal component analysis (PCA)
      • 4.4.1.1. Exact PCA and probabilistic interpretation
      • 4.4.1.2. Approximate PCA
      • 4.4.1.3. Kernel PCA
      • 4.4.1.4. Sparse Principal Components Analysis (SparsePCA and MiniBatchSparsePCA)
    • 4.4.2. Dictionary Learning
      • 4.4.2.1. Sparse coding with a precomputed dictionary
      • 4.4.2.2. Generic dictionary learning
      • 4.4.2.3. Mini-batch dictionary learning
    • 4.4.3. Independent component analysis (ICA)
    • 4.4.4. Non-negative matrix factorization (NMF or NNMF)
  • 4.5. Covariance estimation
    • 4.5.1. Empirical covariance
    • 4.5.2. Shrunk Covariance
      • 4.5.2.1. Basic shrinkage
      • 4.5.2.2. Ledoit-Wolf shrinkage
      • 4.5.2.3. Oracle Approximating Shrinkage
    • 4.5.3. Sparse inverse covariance
    • 4.5.4. Robust Covariance Estimation
      • 4.5.4.1. Minimum Covariance Determinant
  • 4.6. Novelty and Outlier Detection
    • 4.6.1. Novelty Detection
    • 4.6.2. Outlier Detection
      • 4.6.2.1. Fitting an elliptic envelop
      • 4.6.2.2. One-class SVM versus elliptic envelop
  • 4.7. Hidden Markov Models
• 5. Model Selection
  • 5.1. Cross-Validation
    • 5.1.1. Computing cross-validated metrics
    • 5.1.2. Cross validation iterators
      • 5.1.2.1. K-fold
      • 5.1.2.2. Stratified K-Fold
      • 5.1.2.3. Leave-One-Out - LOO
      • 5.1.2.4. Leave-P-Out - LPO
      • 5.1.2.5. Leave-One-Label-Out - LOLO
      • 5.1.2.6. Leave-P-Label-Out
      • 5.1.2.7. Random permutations cross-validation a.k.a. Shuffle & Split
      • 5.1.2.8. Bootstrapping cross-validation
    • 5.1.3. Cross validation and model selection
  • 5.2. Grid Search
    • 5.2.1. GridSearchCV
    • 5.2.2. Examples
    • 5.2.3. Alternatives to brute force grid search
      • 5.2.3.1. Model specific cross-validation
        • 8.15.1.4. sklearn.linear_model.RidgeCV
        • 5.2.3.1.2. sklearn.linear_model.RidgeClassifierCV
        • 8.15.1.11. sklearn.linear_model.LarsCV
        • 8.15.1.12. sklearn.linear_model.LassoLarsCV
        • 8.15.1.6. sklearn.linear_model.LassoCV
        • 8.15.1.8. sklearn.linear_model.ElasticNetCV
      • 5.2.3.2. Information Criterion
        • 8.15.1.13. sklearn.linear_model.LassoLarsIC
      • 5.2.3.3. Out of Bag Estimates
        • 8.6.1. sklearn.ensemble.RandomForestClassifier
        • 8.6.2. sklearn.ensemble.RandomForestRegressor
        • 8.6.3. sklearn.ensemble.ExtraTreesClassifier
        • 8.6.4. sklearn.ensemble.ExtraTreesRegressor
• 6. Dataset transformations
  • 6.1. Preprocessing data
    • 6.1.1. Standardization or Mean Removal and Variance Scaling
    • 6.1.2. Normalization
    • 6.1.3. Binarization
      • 6.1.3.1. Feature binarization
  • 6.2. Feature extraction
    • 6.2.1. Text feature extraction
      • 6.2.1.1. The Bag of Words representation
      • 6.2.1.2. Sparsity
      • 6.2.1.3. Common Vectorizer usage
      • 6.2.1.4. TF-IDF normalization
      • 6.2.1.5. Applications and examples
      • 6.2.1.6. Limitations of the Bag of Words representation
      • 6.2.1.7. Customizing the vectorizer classes
    • 6.2.2. Image feature extraction
      • 6.2.2.1. Patch extraction
  • 6.3. Kernel Approximation
    • 6.3.1. Radial Basis Function Kernel
    • 6.3.2. Additive Chi Squared Kernel
    • 6.3.3. Skewed Chi Squared Kernel
    • 6.3.4. Mathematical Details
• 7. Dataset loading utilities
  • 7.1. General dataset API
  • 7.2. Toy datasets
  • 7.3. Sample images
  • 7.4. Sample generators
  • 7.5. Datasets in svmlight / libsvm format
  • 7.6. The Olivetti faces dataset
  • 7.7. The 20 newsgroups text dataset
    • 7.7.1. Usage
  • 7.8. Downloading datasets from the mldata.org repository
  • 7.9. The Labeled Faces in the Wild face recognition dataset
    • 7.9.1. Usage
    • 7.9.2. Examples
• 8. Reference
  • 8.1. sklearn.cluster: Clustering
    • 8.1.1. sklearn.cluster.AffinityPropagation
    • 8.1.2. sklearn.cluster.DBSCAN
    • 8.1.3. sklearn.cluster.KMeans
    • 8.1.4. sklearn.cluster.MiniBatchKMeans
    • 8.1.5. sklearn.cluster.MeanShift
    • 8.1.6. sklearn.cluster.SpectralClustering
    • 8.1.7. sklearn.cluster.Ward
    • 8.1.8. sklearn.cluster.estimate_bandwidth
  • 8.2. sklearn.covariance: Covariance Estimators
    • 8.2.1. sklearn.covariance.EmpiricalCovariance
    • 8.2.2. sklearn.covariance.ShrunkCovariance
    • 8.2.3. sklearn.covariance.LedoitWolf
    • 8.2.4. sklearn.covariance.OAS
    • 8.2.5. sklearn.covariance.GraphLasso
    • 8.2.6. sklearn.covariance.GraphLassoCV
    • 8.2.7. sklearn.covariance.MinCovDet
    • 8.2.8. sklearn.covariance.empirical_covariance
    • 8.2.9. sklearn.covariance.ledoit_wolf
    • 8.2.10. sklearn.covariance.shrunk_covariance
    • 8.2.11. sklearn.covariance.oas
    • 8.2.12. sklearn.covariance.graph_lasso
  • 8.3. sklearn.cross_validation: Cross Validation
    • 8.3.1. sklearn.cross_validation.Bootstrap
    • 8.3.2. sklearn.cross_validation.KFold
    • 8.3.3. sklearn.cross_validation.LeaveOneLabelOut
    • 8.3.4. sklearn.cross_validation.LeaveOneOut
    • 8.3.5. sklearn.cross_validation.LeavePLabelOut
    • 8.3.6. sklearn.cross_validation.LeavePOut
    • 8.3.7. sklearn.cross_validation.StratifiedKFold
    • 8.3.8. sklearn.cross_validation.ShuffleSplit
    • 8.3.9. sklearn.cross_validation.train_test_split
    • 8.3.10. sklearn.cross_validation.cross_val_score
    • 8.3.11. sklearn.cross_validation.permutation_test_score
    • 8.3.12. sklearn.cross_validation.check_cv
  • 8.4. sklearn.datasets: Datasets
    • 8.4.1. Loaders
      • 8.4.1.1. sklearn.datasets.load_20newsgroups
      • 8.4.1.2. sklearn.datasets.fetch_20newsgroups
      • 8.4.1.3. sklearn.datasets.fetch_20newsgroups_vectorized
      • 8.4.1.4. sklearn.datasets.load_boston
      • 8.4.1.5. sklearn.datasets.load_diabetes
      • 8.4.1.6. sklearn.datasets.load_digits
      • 8.4.1.7. sklearn.datasets.load_files
      • 8.4.1.8. sklearn.datasets.load_iris
      • 8.4.1.9. sklearn.datasets.load_lfw_pairs
      • 8.4.1.10. sklearn.datasets.fetch_lfw_pairs
      • 8.4.1.11. sklearn.datasets.load_lfw_people
      • 8.4.1.12. sklearn.datasets.fetch_lfw_people
      • 8.4.1.13. sklearn.datasets.load_linnerud
      • 8.4.1.14. sklearn.datasets.fetch_olivetti_faces
      • 8.4.1.15. sklearn.datasets.load_sample_image
      • 8.4.1.16. sklearn.datasets.load_sample_images
      • 8.4.1.17. sklearn.datasets.load_svmlight_file
    • 8.4.2. Samples generator
      • 8.4.2.1. sklearn.datasets.make_blobs
      • 8.4.2.2. sklearn.datasets.make_classification
      • 8.4.2.3. sklearn.datasets.make_circles
      • 8.4.2.4. sklearn.datasets.make_friedman1
      • 8.4.2.5. sklearn.datasets.make_friedman2
      • 8.4.2.6. sklearn.datasets.make_friedman3
      • 8.4.2.7. sklearn.datasets.make_low_rank_matrix
      • 8.4.2.8. sklearn.datasets.make_moons
      • 8.4.2.9. sklearn.datasets.make_multilabel_classification
      • 8.4.2.10. sklearn.datasets.make_regression
      • 8.4.2.11. sklearn.datasets.make_s_curve
      • 8.4.2.12. sklearn.datasets.make_sparse_coded_signal
      • 8.4.2.13. sklearn.datasets.make_sparse_spd_matrix
      • 8.4.2.14. sklearn.datasets.make_sparse_uncorrelated
      • 8.4.2.15. sklearn.datasets.make_spd_matrix
      • 8.4.2.16. sklearn.datasets.make_swiss_roll
  • 8.5. sklearn.decomposition: Matrix Decomposition
    • 8.5.1. sklearn.decomposition.PCA
    • 8.5.2. sklearn.decomposition.ProbabilisticPCA
    • 8.5.3. sklearn.decomposition.ProjectedGradientNMF
    • 8.5.4. sklearn.decomposition.RandomizedPCA
    • 8.5.5. sklearn.decomposition.KernelPCA
    • 8.5.6. sklearn.decomposition.FastICA
    • 8.5.7. sklearn.decomposition.NMF
    • 8.5.8. sklearn.decomposition.SparsePCA
    • 8.5.9. sklearn.decomposition.MiniBatchSparsePCA
    • 8.5.10. sklearn.decomposition.SparseCoder
    • 8.5.11. sklearn.decomposition.DictionaryLearning
    • 8.5.12. sklearn.decomposition.MiniBatchDictionaryLearning
    • 8.5.13. sklearn.decomposition.fastica
    • 8.5.14. sklearn.decomposition.dict_learning
    • 8.5.15. sklearn.decomposition.dict_learning_online
    • 8.5.16. sklearn.decomposition.sparse_encode
  • 8.6. sklearn.ensemble: Ensemble Methods
    • 8.6.1. sklearn.ensemble.RandomForestClassifier
    • 8.6.2. sklearn.ensemble.RandomForestRegressor
    • 8.6.3. sklearn.ensemble.ExtraTreesClassifier
    • 8.6.4. sklearn.ensemble.ExtraTreesRegressor
  • 8.7. sklearn.feature_extraction: Feature Extraction
    • 8.7.1. From images
      • 8.7.1.1. sklearn.feature_extraction.image.img_to_graph
      • 8.7.1.2. sklearn.feature_extraction.image.grid_to_graph
      • 8.7.1.3. sklearn.feature_extraction.image.extract_patches_2d
      • 8.7.1.4. sklearn.feature_extraction.image.reconstruct_from_patches_2d
      • 8.7.1.5. sklearn.feature_extraction.image.PatchExtractor
    • 8.7.2. From text
      • 8.7.2.1. sklearn.feature_extraction.text.CountVectorizer
      • 8.7.2.2. sklearn.feature_extraction.text.TfidfTransformer
      • 8.7.2.3. sklearn.feature_extraction.text.Vectorizer
  • 8.8. sklearn.feature_selection: Feature Selection
    • 8.8.1. sklearn.feature_selection.SelectPercentile
    • 8.8.2. sklearn.feature_selection.SelectKBest
    • 8.8.3. sklearn.feature_selection.SelectFpr
    • 8.8.4. sklearn.feature_selection.SelectFdr
    • 8.8.5. sklearn.feature_selection.SelectFwe
    • 8.8.6. sklearn.feature_selection.RFE
    • 8.8.7. sklearn.feature_selection.RFECV
    • 8.8.8. sklearn.feature_selection.chi2
    • 8.8.9. sklearn.feature_selection.f_classif
    • 8.8.10. sklearn.feature_selection.f_regression
  • 8.9. sklearn.gaussian_process: Gaussian Processes
    • 8.9.1. sklearn.gaussian_process.GaussianProcess
    • 8.9.2. sklearn.gaussian_process.correlation_models.absolute_exponential
    • 8.9.3. sklearn.gaussian_process.correlation_models.squared_exponential
    • 8.9.4. sklearn.gaussian_process.correlation_models.generalized_exponential
    • 8.9.5. sklearn.gaussian_process.correlation_models.pure_nugget
    • 8.9.6. sklearn.gaussian_process.correlation_models.cubic
    • 8.9.7. sklearn.gaussian_process.correlation_models.linear
    • 8.9.8. sklearn.gaussian_process.regression_models.constant
    • 8.9.9. sklearn.gaussian_process.regression_models.linear
    • 8.9.10. sklearn.gaussian_process.regression_models.quadratic
  • 8.10. sklearn.grid_search: Grid Search
    • 8.10.1. sklearn.grid_search.GridSearchCV
    • 8.10.2. sklearn.grid_search.IterGrid
  • 8.11. sklearn.hmm: Hidden Markov Models
    • 8.11.1. sklearn.hmm.GaussianHMM
    • 8.11.2. sklearn.hmm.MultinomialHMM
    • 8.11.3. sklearn.hmm.GMMHMM
  • 8.12. sklearn.kernel_approximation Kernel Approximation
    • 8.12.1. sklearn.kernel_approximation.RBFSampler
    • 8.12.2. sklearn.kernel_approximation.AdditiveChi2Sampler
    • 8.12.3. sklearn.kernel_approximation.SkewedChi2Sampler
  • 8.13. sklearn.semi_supervised Semi-Supervised Learning
    • 8.13.1. sklearn.semi_supervised.LabelPropagation
    • 8.13.2. sklearn.semi_supervised.LabelSpreading
  • 8.14. sklearn.lda: Linear Discriminant Analysis
    • 8.14.1. sklearn.lda.LDA
  • 8.15. sklearn.linear_model: Generalized Linear Models
    • 8.15.1. For dense data
      • 8.15.1.1. sklearn.linear_model.LinearRegression
      • 8.15.1.2. sklearn.linear_model.Ridge
      • 8.15.1.3. sklearn.linear_model.RidgeClassifier
      • 8.15.1.4. sklearn.linear_model.RidgeCV
      • 8.15.1.5. sklearn.linear_model.Lasso
      • 8.15.1.6. sklearn.linear_model.LassoCV
      • 8.15.1.7. sklearn.linear_model.ElasticNet
      • 8.15.1.8. sklearn.linear_model.ElasticNetCV
      • 8.15.1.9. sklearn.linear_model.Lars
      • 8.15.1.10. sklearn.linear_model.LassoLars
      • 8.15.1.11. sklearn.linear_model.LarsCV
      • 8.15.1.12. sklearn.linear_model.LassoLarsCV
      • 8.15.1.13. sklearn.linear_model.LassoLarsIC
      • 8.15.2.5. sklearn.linear_model.LogisticRegression
      • 8.15.1.15. sklearn.linear_model.OrthogonalMatchingPursuit
      • 8.15.1.16. sklearn.linear_model.Perceptron
      • 8.15.1.17. sklearn.linear_model.SGDClassifier
      • 8.15.1.18. sklearn.linear_model.SGDRegressor
      • 8.15.1.19. sklearn.linear_model.BayesianRidge
      • 8.15.1.20. sklearn.linear_model.ARDRegression
      • 8.15.1.21. sklearn.linear_model.RandomizedLasso
      • 8.15.1.22. sklearn.linear_model.RandomizedLogisticRegression
      • 8.15.1.23. sklearn.linear_model.lasso_path
      • 8.15.1.24. sklearn.linear_model.lars_path
      • 8.15.1.25. sklearn.linear_model.orthogonal_mp
      • 8.15.1.26. sklearn.linear_model.orthogonal_mp_gram
      • 8.15.1.27. sklearn.linear_model.lasso_stability_path
    • 8.15.2. For sparse data
      • 8.15.2.1. sklearn.linear_model.sparse.Lasso
      • 8.15.2.2. sklearn.linear_model.sparse.ElasticNet
      • 8.15.2.3. sklearn.linear_model.sparse.SGDClassifier
      • 8.15.2.4. sklearn.linear_model.sparse.SGDRegressor
      • 8.15.2.5. sklearn.linear_model.LogisticRegression
  • 8.16. sklearn.manifold: Manifold Learning
    • 8.16.1. sklearn.manifold.LocallyLinearEmbedding
    • 8.16.2. sklearn.manifold.Isomap
    • 8.16.3. sklearn.manifold.locally_linear_embedding
  • 8.17. sklearn.metrics: Metrics
    • 8.17.1. Classification metrics
      • 8.17.1.1. sklearn.metrics.confusion_matrix
      • 8.17.1.2. sklearn.metrics.roc_curve
      • 8.17.1.3. sklearn.metrics.auc
      • 8.17.1.4. sklearn.metrics.precision_score
      • 8.17.1.5. sklearn.metrics.recall_score
      • 8.17.1.6. sklearn.metrics.fbeta_score
      • 8.17.1.7. sklearn.metrics.f1_score
      • 8.17.1.8. sklearn.metrics.precision_recall_fscore_support
      • 8.17.1.9. sklearn.metrics.classification_report
      • 8.17.1.10. sklearn.metrics.precision_recall_curve
      • 8.17.1.11. sklearn.metrics.zero_one_score
      • 8.17.1.12. sklearn.metrics.zero_one
      • 8.17.1.13. sklearn.metrics.hinge_loss
    • 8.17.2. Regression metrics
      • 8.17.2.1. sklearn.metrics.r2_score
      • 8.17.2.2. sklearn.metrics.mean_squared_error
    • 8.17.3. Clustering metrics
      • 8.17.3.1. sklearn.metrics.adjusted_rand_score
      • 8.17.3.2. sklearn.metrics.adjusted_mutual_info_score
      • 8.17.3.3. sklearn.metrics.homogeneity_completeness_v_measure
      • 8.17.3.4. sklearn.metrics.homogeneity_score
      • 8.17.3.5. sklearn.metrics.completeness_score
      • 8.17.3.6. sklearn.metrics.v_measure_score
      • 8.17.3.7. sklearn.metrics.silhouette_score
    • 8.17.4. Pairwise metrics
      • 8.17.4.1. sklearn.metrics.pairwise.euclidean_distances
      • 8.17.4.2. sklearn.metrics.pairwise.manhattan_distances
      • 8.17.4.3. sklearn.metrics.pairwise.linear_kernel
      • 8.17.4.4. sklearn.metrics.pairwise.polynomial_kernel
      • 8.17.4.5. sklearn.metrics.pairwise.rbf_kernel
      • 8.17.4.6. sklearn.metrics.pairwise.distance_metrics
      • 8.17.4.7. sklearn.metrics.pairwise.pairwise_distances
      • 8.17.4.8. sklearn.metrics.pairwise.kernel_metrics
      • 8.17.4.9. sklearn.metrics.pairwise.pairwise_kernels
  • 8.18. sklearn.mixture: Gaussian Mixture Models
    • 8.18.1. sklearn.mixture.GMM
    • 8.18.2. sklearn.mixture.DPGMM
    • 8.18.3. sklearn.mixture.VBGMM
  • 8.19. sklearn.multiclass: Multiclass and multilabel classification
    • 8.19.1. Multiclass and multilabel classification strategies
    • 8.19.2. sklearn.multiclass.OneVsRestClassifier
    • 8.19.3. sklearn.multiclass.OneVsOneClassifier
    • 8.19.4. sklearn.multiclass.OutputCodeClassifier
    • 8.19.5. sklearn.multiclass.fit_ovr
    • 8.19.6. sklearn.multiclass.predict_ovr
    • 8.19.7. sklearn.multiclass.fit_ovo
    • 8.19.8. sklearn.multiclass.predict_ovo
    • 8.19.9. sklearn.multiclass.fit_ecoc
    • 8.19.10. sklearn.multiclass.predict_ecoc
  • 8.20. sklearn.naive_bayes: Naive Bayes
    • 8.20.1. sklearn.naive_bayes.GaussianNB
    • 8.20.2. sklearn.naive_bayes.MultinomialNB
    • 8.20.3. sklearn.naive_bayes.BernoulliNB
  • 8.21. sklearn.neighbors: Nearest Neighbors
    • 8.21.1. sklearn.neighbors.NearestNeighbors
    • 8.21.2. sklearn.neighbors.KNeighborsClassifier
    • 8.21.3. sklearn.neighbors.RadiusNeighborsClassifier
    • 8.21.4. sklearn.neighbors.KNeighborsRegressor
    • 8.21.5. sklearn.neighbors.RadiusNeighborsRegressor
    • 8.21.6. sklearn.neighbors.BallTree
    • 8.21.7. sklearn.neighbors.kneighbors_graph
    • 8.21.8. sklearn.neighbors.radius_neighbors_graph
  • 8.22. sklearn.pls: Partial Least Squares
    • 8.22.1. sklearn.pls.PLSRegression
    • 8.22.2. sklearn.pls.PLSCanonical
    • 8.22.3. sklearn.pls.CCA
    • 8.22.4. sklearn.pls.PLSSVD
  • 8.23. sklearn.pipeline: Pipeline
    • 8.23.1. sklearn.pipeline.Pipeline
  • 8.24. sklearn.preprocessing: Preprocessing and Normalization
    • 8.24.1. sklearn.preprocessing.Scaler
    • 8.24.2. sklearn.preprocessing.Normalizer
    • 8.24.3. sklearn.preprocessing.Binarizer
    • 8.24.4. sklearn.preprocessing.LabelBinarizer
    • 8.24.5. sklearn.preprocessing.KernelCenterer
    • 8.24.6. sklearn.preprocessing.scale
    • 8.24.7. sklearn.preprocessing.normalize
    • 8.24.8. sklearn.preprocessing.binarize
  • 8.25. sklearn.qda: Quadratic Discriminant Analysis
    • 8.25.1. sklearn.qda.QDA
  • 8.26. sklearn.svm: Support Vector Machines
    • 8.26.1. Estimators
      • 8.26.1.1. sklearn.svm.SVC
      • 8.26.1.2. sklearn.svm.LinearSVC
      • 8.26.1.3. sklearn.svm.NuSVC
      • 8.26.1.4. sklearn.svm.SVR
      • 8.26.1.5. sklearn.svm.NuSVR
      • 8.26.1.6. sklearn.svm.OneClassSVM
      • 8.26.1.7. sklearn.svm.l1_min_c
    • 8.26.2. Low-level methods
      • 8.26.2.1. sklearn.svm.libsvm.fit
      • 8.26.2.2. sklearn.svm.libsvm.decision_function
      • 8.26.2.3. sklearn.svm.libsvm.predict
      • 8.26.2.4. sklearn.svm.libsvm.predict_proba
      • 8.26.2.5. sklearn.svm.libsvm.cross_validation
  • 8.27. sklearn.tree: Decision Trees
    • 8.27.1. sklearn.tree.DecisionTreeClassifier
    • 8.27.2. sklearn.tree.DecisionTreeRegressor
    • 8.27.3. sklearn.tree.ExtraTreeClassifier
    • 8.27.4. sklearn.tree.ExtraTreeRegressor
    • 8.27.5. sklearn.tree.export_graphviz
  • 8.28. sklearn.utils: Utilities
    • 8.28.1. sklearn.utils.check_random_state
    • 8.28.2. sklearn.utils.resample
    • 8.28.3. sklearn.utils.shuffle