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1. Background
2. scikit-learn

1. Background

Look at core scikit-learn features on toy data as a spike for preprocessing data cleanup in a machien learning pipeline.

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

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. linearmodel

2.7. featureextraction

2.8. featureselection

2.9. metrics

2.10. ensemble

2.11. modelselection

2.12. covariance

2.13. neighbors

2.14. naivebayes

2.15. neuralnetwork

2.16. pipeline

2.17. cluster

2.18. tree

2.19. gaussianprocess

2.20. kernelridge

2.21. randomprojection

2.22. crossdecomposition

2.23. calibration

2.24. semisupervised

2.25. svm

2.26. multioutput

2.27. manifold

2.28. multiclass

2.29. mixture

2.30. discriminantanalysis

2.31. kernelapproximation

2.32. isotonic

2.33. decomposition

2.6. linear_model

2.7. feature_extraction

2.8. feature_selection

2.11. model_selection

2.14. naive_bayes

2.15. neural_network

2.19. gaussian_process

2.20. kernel_ridge

2.21. random_projection

2.22. cross_{decomposition}

2.24. semi_supervised

2.30. discriminant_analysis

2.31. kernel_{approximation}