Contents

Section Title Page
1 Introduction 4
2 What is H2O? 4
3 Installation 5
3.1 Installation in R 5
3.2 Installation in Python 6
3.3 Pointing to a Different H2O Cluster 7
3.4 Example Code 7
3.5 Citation 7
4 Overview 8
4.1 Summary of Features 8
4.2 Theory and Framework 9
4.3 Distributed Trees 10
4.4 Treatment of Factors 11
4.5 Key Parameters 12
4.5.1 Convergence-based Early Stopping 13
4.5.2 Time-based Early Stopping 13
4.5.3 Stochastic GBM 13
4.5.4 Distributions and Loss Functions 14
5 Use Case: Airline Data Classification on
5.1 Loading Data 15
5.2 Performing a Trial Run 16
5.3 Extracting and Handling the Results 19
5.4 Web Interface 20
5.5 Variable Importances 20
5.6 Supported Output 20
5.7 Java Models 21
5.8 Grid Search for Model Comparison 21
5.8.1 Cartesian Grid Search 21
5.8.2 Random Grid Search 23
6 Model Parameters 24
7 Acknowledgments 28
8 References 29
9 Authors 30

 

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