- 1. Installing scikits.learn
- 2. Getting started: an introduction to machine learning with scikits.learn
- 3. Supervised learning
- 3.1. Generalized Linear Models
- 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
- 3.2.8. Mathematical formulation
- 3.2.9. Frequently Asked Questions
- 3.2.10. Implementation details
- 3.3. Stochastic Gradient Descent
- 3.4. Nearest Neighbors
- 3.5. Feature selection
- 3.6. Gaussian Processes
- 4. Unsupervised learning
- 5. Model Selection
- 6. Class Reference
- 6.1. Support Vector Machines
- 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
- 6.2.1. scikits.learn.linear_model.LinearRegression
- 6.3. Bayesian Regression
- 6.4. Naive Bayes
- 6.5. Nearest Neighbors
- 6.6. Gaussian Mixture Models
- 6.7. Hidden Markov Models
- 6.8. Clustering
- 6.9. Covariance Estimators
- 6.10. Signal Decomposition
- 6.11. Cross Validation
- 6.12. Grid Search
- 6.13. Feature Selection
- 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
- 6.15. Pipeline