Python sklearn.linear_model Module

This page shows the popular functions and classes defined in the sklearn.linear_model module. The items are ordered by their popularity in 40,000 open source Python projects. If you can not find a good example below, you can try the search function to search modules.

• 1. LogisticRegression()    
  Used in 429 projects
• 2. LinearRegression()    
  Used in 249 projects
• 3. Ridge()    
  Used in 91 projects
• 4. SGDClassifier()    
  Used in 88 projects
• 5. Lasso()    
  Used in 65 projects
• 6. ElasticNet()    
  Used in 31 projects
• 7. LogisticRegressionCV()    
  Used in 27 projects
• 8. SGDRegressor()    
  Used in 26 projects
• 9. RidgeCV()    
  Used in 22 projects
• 10. BayesianRidge()    
  Used in 21 projects
• 11. LassoCV()    
  Used in 21 projects
• 12. Perceptron()    
  Used in 20 projects
• 13. RANSACRegressor()    
  Used in 19 projects
• 14. ElasticNetCV()    
  Used in 18 projects
• 15. PassiveAggressiveClassifier()    
  Used in 16 projects
• 16. RidgeClassifier()    
  Used in 15 projects
• 17. HuberRegressor()    
  Used in 13 projects
• 18. ARDRegression()    
  Used in 11 projects
• 19. LassoLars()    
  Used in 11 projects
• 20. Lars()    
  Used in 9 projects
• 21. LassoLarsCV()    
  Used in 9 projects
• 22. PassiveAggressiveRegressor()    
  Used in 9 projects
• 23. MultiTaskLasso()    
  Used in 8 projects
• 24. OrthogonalMatchingPursuit()    
  Used in 7 projects
• 25. RidgeClassifierCV()    
  Used in 6 projects
• 26. RandomizedLogisticRegression()    
  Used in 5 projects
• 27. TheilSenRegressor()    
  Used in 5 projects
• 28. LassoLarsIC()    
  Used in 5 projects
• 29. MultiTaskElasticNet()    
  Used in 5 projects
• 30. LarsCV()    
  Used in 5 projects