Machine Learning with Python and H2O
May 2020: Fifth Edition
Contents
| Section | Title | Page |
|---|---|---|
| 1 | Introduction | 4 |
| 2 | What is H2O? | 5 |
| 2.1 | Example Code | 6 |
| 2.2 | Citation | 6 |
| 3 | Installation | 6 |
| 3.1 | Installation in Python | 7 |
| 4 | Data Preparation | 7 |
| 4.1 | Viewing Data | 9 |
| 4.2 | Selection | 10 |
| 4.3 | Missing Data | 12 |
| 4.4 | Operations | 13 |
| 4.5 | Merging | 16 |
| 4.6 | Grouping | 17 |
| 4.7 | Using Date and Time Data | 18 |
| 4.8 | Categoricals | 19 |
| 4.9 | Loading and Saving Data | 21 |
| 5 | Machine Learning | 21 |
| 5.1 | Modeling | 21 |
| 5.1.1 | Supervised Learning | 22 |
| 5.1.2 | Unsupervised Learning | 23 |
| 5.1.3 | Miscellaneous | 23 |
| 5.2 | Running Models | 23 |
| 5.2.1 | Gradient Boosting Machine (GBM) | 24 |
| 5.2.2 | Generalized Linear Models (GLM) | 27 |
| 5.2.3 | K-means | 30 |
| 5.2.4 | Principal Components Analysis (PCA) | 32 |
| 5.3 | Grid Search | 33 |
| 5.4 | Integration with scikit-learn | 34 |
| 5.4.1 | Pipelines | 34 |
| 5.4.2 | Randomized Grid Search | 36 |
| 6 | Acknowledgments | 38 |
| 7 | References | 38 |
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