scikit-learn
Table of Contents
- 1. Background
- 2. scikit-learn
- 2.1. datasets
- 2.2. random
- 2.3. utils
- 2.4. exceptions
- 2.5. prepocessing
- 2.6. linear_model
- 2.7. feature_extraction
- 2.8. feature_selection
- 2.9. metrics
- 2.10. ensemble
- 2.11. model_selection
- 2.12. covariance
- 2.13. neighbors
- 2.14. naive_bayes
- 2.15. neural_network
- 2.16. pipeline
- 2.17. cluster
- 2.18. tree
- 2.19. gaussian_process
- 2.20. kernel_ridge
- 2.21. random_projection
- 2.22. cross_decomposition
- 2.23. calibration
- 2.24. semi_supervised
- 2.25. svm
- 2.26. multioutput
- 2.27. manifold
- 2.28. multiclass
- 2.29. mixture
- 2.30. discriminant_analysis
- 2.31. kernel_approximation
- 2.32. isotonic
- 2.33. decomposition
1. Background
Look at core scikit-learn features on toy data as a spike for preprocessing data cleanup in a machien learning pipeline.