User guide: contents

This is the html version of user guide. A PDF version for printing can be found here.

• 1. Installing scikits.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
  • 1.2. Third party distributions of scikits.learn
    • 1.2.1. Debian and derivatives (Ubuntu)
    • 1.2.2. Enthought python distribution
    • 1.2.3. Macports
  • 1.3. Bleeding Edge
  • 1.4. Testing
• 2. Getting started: an introduction to machine learning with scikits.learn
  • 2.1. Machine learning: the problem setting
  • 2.2. Loading an example dataset
  • 2.3. Learning and Predicting
• 3. Supervised learning
  • 3.1. Generalized Linear Models
    • 3.1.1. Ordinary Least Squares (OLS)
      • 3.1.1.1. OLS Complexity
    • 3.1.2. Ridge Regression
      • 3.1.2.1. Ridge Complexity
    • 3.1.3. Lasso
    • 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. Bayesian Regression
      • 3.1.7.1. Bayesian Ridge Regression
    • 3.1.8. Automatic Relevance Determination - ARD
      • 3.1.8.1. Mathematical formulation
    • 3.1.9. Stochastic Gradient Descent - SGD
  • 3.2. Support Vector Machines
    • 3.2.1. Classification
    • 3.2.2. Regression
    • 3.2.3. Density estimation, outliers detection
    • 3.2.4. Support Vector machines for sparse data
    • 3.2.5. Complexity
    • 3.2.6. Tips on Practical Use
    • 3.2.7. Kernel functions
      • 3.2.7.1. Custom Kernels
        • 3.2.7.1.1. Using python functions as kernels
        • 3.2.7.1.2. Using the Gram matrix
    • 3.2.8. Mathematical formulation
      • 3.2.8.1. SVC
      • 3.2.8.2. NuSVC
    • 3.2.9. Frequently Asked Questions
    • 3.2.10. 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. Classification
    • 3.4.2. Regression
    • 3.4.3. Efficient implementation: the ball tree
  • 3.5. Feature selection
    • 3.5.1. Univariate feature selection
      • 3.5.1.1. Feature scoring functions
        • 3.5.1.1.1. For classification
        • 3.5.1.1.2. For regression
  • 3.6. Gaussian Processes
    • 3.6.1. An introductory regression example
    • 3.6.2. Mathematical formulation
      • 3.6.2.1. The initial assumption
      • 3.6.2.2. The best linear unbiased prediction (BLUP)
      • 3.6.2.3. The empirical best linear unbiased predictor (EBLUP)
    • 3.6.3. Correlation Models
    • 3.6.4. Regression Models
    • 3.6.5. Implementation details
• 4. Unsupervised learning
  • 4.1. Gaussian mixture models
    • 4.1.1. GMM classifier
  • 4.2. Clustering
    • 4.2.1. Affinity propagation
    • 4.2.2. Mean Shift
    • 4.2.3. K-means
    • 4.2.4. Spectral clustering
  • 4.3. Decomposing signals in components (matrix factorization problems)
    • 4.3.1. Principal component analysis (PCA)
    • 4.3.2. Independent component analysis (ICA)
• 5. Model Selection
  • 5.1. Cross-Validation
    • 5.1.1. Examples
      • 5.1.1.1. Leave-One-Out - LOO
      • 5.1.1.2. Leave-P-Out - LPO
      • 5.1.1.3. K-fold
      • 5.1.1.4. Stratified K-Fold
      • 5.1.1.5. Leave-One-Label-Out - LOLO
      • 5.1.1.6. Leave-P-Label-Out
  • 5.2. Grid Search
    • 5.2.1. Examples
• 6. Class Reference
  • 6.1. Support Vector Machines
    • 6.1.1. scikits.learn.svm.SVC
    • 6.1.2. scikits.learn.svm.LinearSVC
    • 6.1.3. scikits.learn.svm.NuSVC
    • 6.1.4. scikits.learn.svm.SVR
    • 6.1.5. scikits.learn.svm.NuSVR
    • 6.1.6. scikits.learn.svm.OneClassSVM
    • 6.1.7. For sparse data
      • 6.1.7.1. scikits.learn.svm.sparse.SVC
      • 6.1.7.2. scikits.learn.svm.sparse.NuSVC
      • 6.1.7.3. scikits.learn.svm.sparse.SVR
      • 6.1.7.4. scikits.learn.svm.sparse.NuSVR
      • 6.1.7.5. scikits.learn.svm.sparse.OneClassSVM
      • 6.1.7.6. scikits.learn.svm.sparse.LinearSVC
  • 6.2. Generalized Linear Models
    • 6.2.1. scikits.learn.linear_model.LinearRegression
    • 6.2.2. scikits.learn.linear_model.Ridge
    • 6.2.3. scikits.learn.linear_model.Lasso
    • 6.2.4. scikits.learn.linear_model.LassoCV
    • 6.2.5. scikits.learn.linear_model.ElasticNet
    • 6.2.6. scikits.learn.linear_model.ElasticNetCV
    • 6.2.7. scikits.learn.linear_model.LARS
    • 6.2.8. scikits.learn.linear_model.LassoLARS
    • 6.2.9. scikits.learn.linear_model.LogisticRegression
    • 6.2.10. scikits.learn.linear_model.SGDClassifier
    • 6.2.11. scikits.learn.linear_model.SGDRegressor
    • 6.2.12. scikits.learn.linear_model.lasso_path
    • 6.2.13. scikits.learn.linear_model.lars_path
    • 6.2.14. For sparse data
      • 6.2.14.1. scikits.learn.linear_model.sparse.Lasso
      • 6.2.14.2. scikits.learn.linear_model.sparse.ElasticNet
      • 6.2.14.3. scikits.learn.linear_model.sparse.SGDClassifier
      • 6.2.14.4. scikits.learn.linear_model.sparse.SGDRegressor
  • 6.3. Bayesian Regression
    • 6.3.1. scikits.learn.linear_model.BayesianRidge
    • 6.3.2. scikits.learn.linear_model.ARDRegression
  • 6.4. Naive Bayes
    • 6.4.1. scikits.learn.naive_bayes.GNB
  • 6.5. Nearest Neighbors
    • 6.5.1. scikits.learn.neighbors.Neighbors
    • 6.5.2. scikits.learn.neighbors.NeighborsBarycenter
    • 6.5.3. scikits.learn.ball_tree.BallTree
    • 6.5.4. scikits.learn.ball_tree.knn_brute
  • 6.6. Gaussian Mixture Models
    • 6.6.1. scikits.learn.mixture.GMM
  • 6.7. Hidden Markov Models
    • 6.7.1. scikits.learn.hmm.GaussianHMM
    • 6.7.2. scikits.learn.hmm.MultinomialHMM
    • 6.7.3. scikits.learn.hmm.GMMHMM
  • 6.8. Clustering
    • 6.8.1. scikits.learn.cluster.KMeans
    • 6.8.2. scikits.learn.cluster.MeanShift
    • 6.8.3. scikits.learn.cluster.SpectralClustering
    • 6.8.4. scikits.learn.cluster.AffinityPropagation
  • 6.9. Covariance Estimators
    • 6.9.1. scikits.learn.covariance.Covariance
    • 6.9.2. scikits.learn.covariance.ShrunkCovariance
    • 6.9.3. scikits.learn.covariance.LedoitWolf
    • 6.9.4. scikits.learn.covariance.ledoit_wolf
  • 6.10. Signal Decomposition
    • 6.10.1. scikits.learn.pca.PCA
    • 6.10.2. scikits.learn.pca.ProbabilisticPCA
    • 6.10.3. scikits.learn.fastica.FastICA
  • 6.11. Cross Validation
    • 6.11.1. scikits.learn.cross_val.LeaveOneOut
    • 6.11.2. scikits.learn.cross_val.LeavePOut
    • 6.11.3. scikits.learn.cross_val.KFold
    • 6.11.4. scikits.learn.cross_val.StratifiedKFold
    • 6.11.5. scikits.learn.cross_val.LeaveOneLabelOut
    • 6.11.6. scikits.learn.cross_val.LeavePLabelOut
  • 6.12. Grid Search
    • 6.12.1. scikits.learn.grid_search.GridSearchCV
  • 6.13. Feature Selection
    • 6.13.1. scikits.learn.feature_selection.rfe.RFE
    • 6.13.2. scikits.learn.feature_selection.rfe.RFECV
  • 6.14. Feature Extraction
    • 6.14.1. scikits.learn.feature_extraction.image.img_to_graph
    • 6.14.2. scikits.learn.feature_extraction.text.RomanPreprocessor
    • 6.14.3. scikits.learn.feature_extraction.text.WordNGramAnalyzer
    • 6.14.4. scikits.learn.feature_extraction.text.CharNGramAnalyzer
    • 6.14.5. scikits.learn.feature_extraction.text.CountVectorizer
    • 6.14.6. scikits.learn.feature_extraction.text.TfidfTransformer
    • 6.14.7. scikits.learn.feature_extraction.text.Vectorizer
    • 6.14.8. For sparse data
      • 6.14.8.1. scikits.learn.feature_extraction.text.sparse.TfidfTransformer
      • 6.14.8.2. scikits.learn.feature_extraction.text.sparse.CountVectorizer
      • 6.14.8.3. scikits.learn.feature_extraction.text.sparse.Vectorizer
  • 6.15. Pipeline
    • 6.15.1. scikits.learn.pipeline.Pipeline