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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