User guide: contents¶
- 1. Installing scikit-learn
- 2. Getting started: an introduction to machine learning with scikit-learn
- 3. Supervised learning
- 3.1. Generalized Linear Models
- 3.1.1. Ordinary Least Squares
- 3.1.2. Ridge Regression
- 3.1.3. Lasso
- 3.1.4. Elastic Net
- 3.1.5. Least Angle Regression
- 3.1.6. LARS Lasso
- 3.1.7. Orthogonal Matching Pursuit (OMP)
- 3.1.8. Bayesian Regression
- 3.1.9. Automatic Relevance Determination - ARD
- 3.1.10. Logisitic regression
- 3.1.11. Stochastic Gradient Descent - SGD
- 3.2. Support Vector Machines
- 3.3. Stochastic Gradient Descent
- 3.4. Nearest Neighbors
- 3.5. Gaussian Processes
- 3.6. Partial Least Squares
- 3.7. Naive Bayes
- 3.8. Multiclass algorithms
- 3.9. Feature selection
- 3.1. Generalized Linear Models
- 4. Unsupervised learning
- 4.1. Gaussian mixture models
- 4.2. Manifold learning
- 4.3. Clustering
- 4.4. Decomposing signals in components (matrix factorization problems)
- 4.5. Covariance estimation
- 4.6. Hidden Markov Models
- 5. Model Selection
- 6. Dataset loading utilities
- 7. Preprocessing data
- 8. Feature extraction
- 9. Class Reference
- 9.1. Support Vector Machines
- 9.2. Generalized Linear Models
- 9.2.1. sklearn.linear_model.LinearRegression
- 9.2.2. sklearn.linear_model.Ridge
- 9.2.3. sklearn.linear_model.RidgeCV
- 9.2.4. sklearn.linear_model.Lasso
- 9.2.5. sklearn.linear_model.LassoCV
- 9.2.6. sklearn.linear_model.ElasticNet
- 9.2.7. sklearn.linear_model.ElasticNetCV
- 9.2.8. sklearn.linear_model.Lars
- 9.2.9. sklearn.linear_model.LassoLars
- 9.2.10. sklearn.linear_model.LarsCV
- 9.2.11. sklearn.linear_model.LassoLarsCV
- 9.2.12. sklearn.linear_model.LassoLarsIC
- 9.2.13. sklearn.linear_model.LogisticRegression
- 9.2.14. sklearn.linear_model.OrthogonalMatchingPursuit
- 9.2.15. sklearn.linear_model.SGDClassifier
- 9.2.16. sklearn.linear_model.SGDRegressor
- 9.2.17. sklearn.linear_model.BayesianRidge
- 9.2.18. sklearn.linear_model.ARDRegression
- 9.2.19. sklearn.linear_model.lasso_path
- 9.2.20. sklearn.linear_model.lars_path
- 9.2.21. sklearn.linear_model.orthogonal_mp
- 9.2.22. sklearn.linear_model.orthogonal_mp_gram
- 9.2.23. For sparse data
- 9.3. Naive Bayes
- 9.4. Nearest Neighbors
- 9.4.1. sklearn.neighbors.NearestNeighbors
- 9.4.2. sklearn.neighbors.KNeighborsClassifier
- 9.4.3. sklearn.neighbors.RadiusNeighborsClassifier
- 9.4.4. sklearn.neighbors.NeighborsClassifier
- 9.4.5. sklearn.neighbors.KNeighborsRegressor
- 9.4.6. sklearn.neighbors.RadiusNeighborsRegressor
- 9.4.7. sklearn.neighbors.NeighborsRegressor
- 9.4.8. sklearn.neighbors.BallTree
- 9.4.9. sklearn.neighbors.kneighbors_graph
- 9.4.10. sklearn.neighbors.radius_neighbors_graph
- 9.5. Gaussian Mixture Models
- 9.6. Hidden Markov Models
- 9.7. Clustering
- 9.8. Metrics
- 9.8.1. Classification metrics
- 9.8.1.1. sklearn.metrics.confusion_matrix
- 9.8.1.2. sklearn.metrics.roc_curve
- 9.8.1.3. sklearn.metrics.auc
- 9.8.1.4. sklearn.metrics.precision_score
- 9.8.1.5. sklearn.metrics.recall_score
- 9.8.1.6. sklearn.metrics.fbeta_score
- 9.8.1.7. sklearn.metrics.f1_score
- 9.8.1.8. sklearn.metrics.precision_recall_fscore_support
- 9.8.1.9. sklearn.metrics.classification_report
- 9.8.1.10. sklearn.metrics.precision_recall_curve
- 9.8.1.11. sklearn.metrics.zero_one_score
- 9.8.1.12. sklearn.metrics.zero_one
- 9.8.1.13. sklearn.metrics.hinge_loss
- 9.8.2. Regression metrics
- 9.8.3. Clustering metrics
- 9.8.4. Pairwise metrics
- 9.8.4.1. sklearn.metrics.pairwise.euclidean_distances
- 9.8.4.2. sklearn.metrics.pairwise.linear_kernel
- 9.8.4.3. sklearn.metrics.pairwise.polynomial_kernel
- 9.8.4.4. sklearn.metrics.pairwise.rbf_kernel
- 9.8.4.5. sklearn.metrics.pairwise.distance_metrics
- 9.8.4.6. sklearn.metrics.pairwise.pairwise_distances
- 9.8.4.7. sklearn.metrics.pairwise.kernel_metrics
- 9.8.4.8. sklearn.metrics.pairwise.pairwise_kernels
- 9.8.1. Classification metrics
- 9.9. Covariance Estimators
- 9.9.1. Covariance estimators
- 9.9.2. sklearn.covariance.EmpiricalCovariance
- 9.9.3. sklearn.covariance.ShrunkCovariance
- 9.9.4. sklearn.covariance.LedoitWolf
- 9.9.5. sklearn.covariance.OAS
- 9.9.6. sklearn.covariance.empirical_covariance
- 9.9.7. sklearn.covariance.ledoit_wolf
- 9.9.8. sklearn.covariance.shrunk_covariance
- 9.9.9. sklearn.covariance.oas
- 9.10. Signal Decomposition
- 9.10.1. sklearn.decomposition.PCA
- 9.10.2. sklearn.decomposition.ProbabilisticPCA
- 9.10.3. sklearn.decomposition.ProjectedGradientNMF
- 9.10.4. sklearn.decomposition.RandomizedPCA
- 9.10.5. sklearn.decomposition.KernelPCA
- 9.10.6. sklearn.decomposition.FastICA
- 9.10.7. sklearn.decomposition.NMF
- 9.10.8. sklearn.decomposition.SparsePCA
- 9.10.9. sklearn.decomposition.MiniBatchSparsePCA
- 9.10.10. sklearn.decomposition.DictionaryLearning
- 9.10.11. sklearn.decomposition.MiniBatchDictionaryLearning
- 9.10.12. sklearn.decomposition.fastica
- 9.10.13. sklearn.decomposition.dict_learning
- 9.10.14. sklearn.decomposition.dict_learning_online
- 9.10.15. sklearn.decomposition.sparse_encode
- 9.10.16. sklearn.decomposition.sparse_encode_parallel
- 9.11. Linear Discriminant Analysis
- 9.12. Partial Least Squares
- 9.13. Cross Validation
- 9.13.1. sklearn.cross_validation.LeaveOneOut
- 9.13.2. sklearn.cross_validation.LeavePOut
- 9.13.3. sklearn.cross_validation.KFold
- 9.13.4. sklearn.cross_validation.StratifiedKFold
- 9.13.5. sklearn.cross_validation.LeaveOneLabelOut
- 9.13.6. sklearn.cross_validation.LeavePLabelOut
- 9.13.7. sklearn.cross_validation.Bootstrap
- 9.13.8. sklearn.cross_validation.ShuffleSplit
- 9.14. Grid Search
- 9.15. Feature Selection
- 9.16. Feature Extraction
- 9.16.1. From images
- 9.16.1.1. sklearn.feature_extraction.image.img_to_graph
- 9.16.1.2. sklearn.feature_extraction.image.grid_to_graph
- 9.16.1.3. sklearn.feature_extraction.image.extract_patches_2d
- 9.16.1.4. sklearn.feature_extraction.image.reconstruct_from_patches_2d
- 9.16.1.5. sklearn.feature_extraction.image.PatchExtractor
- 9.16.2. From text
- 9.16.2.1. sklearn.feature_extraction.text.RomanPreprocessor
- 9.16.2.2. sklearn.feature_extraction.text.WordNGramAnalyzer
- 9.16.2.3. sklearn.feature_extraction.text.CharNGramAnalyzer
- 9.16.2.4. sklearn.feature_extraction.text.CountVectorizer
- 9.16.2.5. sklearn.feature_extraction.text.TfidfTransformer
- 9.16.2.6. sklearn.feature_extraction.text.Vectorizer
- 9.16.1. From images
- 9.17. Preprocessing and normalization
- 9.17.1. sklearn.preprocessing.Scaler
- 9.17.2. sklearn.preprocessing.Normalizer
- 9.17.3. sklearn.preprocessing.Binarizer
- 9.17.4. sklearn.preprocessing.LabelBinarizer
- 9.17.5. sklearn.preprocessing.KernelCenterer
- 9.17.6. sklearn.preprocessing.scale
- 9.17.7. sklearn.preprocessing.normalize
- 9.17.8. sklearn.preprocessing.binarize
- 9.18. Manifold learning
- 9.19. Datasets
- 9.19.1. Loaders
- 9.19.1.1. sklearn.datasets.load_files
- 9.19.1.2. sklearn.datasets.load_diabetes
- 9.19.1.3. sklearn.datasets.load_digits
- 9.19.1.4. sklearn.datasets.load_iris
- 9.19.1.5. sklearn.datasets.load_linnerud
- 9.19.1.6. sklearn.datasets.load_lfw_pairs
- 9.19.1.7. sklearn.datasets.fetch_lfw_pairs
- 9.19.1.8. sklearn.datasets.load_lfw_people
- 9.19.1.9. sklearn.datasets.fetch_lfw_people
- 9.19.1.10. sklearn.datasets.load_20newsgroups
- 9.19.1.11. sklearn.datasets.fetch_20newsgroups
- 9.19.2. Samples generator
- 9.19.2.1. sklearn.datasets.make_classification
- 9.19.2.2. sklearn.datasets.make_regression
- 9.19.2.3. sklearn.datasets.make_blobs
- 9.19.2.4. sklearn.datasets.make_friedman1
- 9.19.2.5. sklearn.datasets.make_friedman2
- 9.19.2.6. sklearn.datasets.make_friedman3
- 9.19.2.7. sklearn.datasets.make_low_rank_matrix
- 9.19.2.8. sklearn.datasets.make_sparse_coded_signal
- 9.19.2.9. sklearn.datasets.make_sparse_uncorrelated
- 9.19.2.10. sklearn.datasets.make_spd_matrix
- 9.19.2.11. sklearn.datasets.make_swiss_roll
- 9.19.2.12. sklearn.datasets.make_s_curve
- 9.19.1. Loaders
- 9.20. Pipeline
- 9.21. Utilities