Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI

300 Pages·2016·8.5 MB·English

by  Darren Cook

Practical Machine
Learning with
H 2O
POWERFUL, SCALABLE
TECHNIQUES FOR AI
AND DEEP LEARNING
Darren Cook

Practical Machine Learning
with H2O
Powerful, Scalable Techniques
for Deep Learning and AI
Darren Cook
BBeeiijjiinngg BBoossttoonn FFaarrnnhhaamm SSeebbaassttooppooll TTookkyyoo
Practical Machine Learning with H2O
by Darren Cook
Copyright © 2017 Darren Cook. All rights reserved.
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978-1-491-96460-6
[LSI]
Table of Contents
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
1. Installation and Quick-Start. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Preparing to Install 1
Installing R 2
Installing Python 2
Privacy 3
Installing Java 3
Install H2O with R (CRAN) 3
Install H2O with Python (pip) 5
Our First Learning 7
Training and Predictions, with Python 11
Training and Predictions, with R 13
Performance Versus Predictions 15
On Being Unlucky 16
Flow 17
Data 17
Models 20
Predictions 20
Other Things in Flow 22
Summary 23
2. Data Import, Data Export. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Memory Requirements 25
Preparing the Data 26
Getting Data into H2O 28
Load CSV Files 28
Load Other File Formats 30
iii
Load Directly from R 31
Load Directly from Python 32
Data Manipulation 34
Laziness, Naming, Deleting 34
Data Summaries 36
Operations on Columns 36
Aggregating Rows 37
Indexing 39
Split Data Already in H2O 41
Rows and Columns 45
Getting Data Out of H2O 48
Exporting Data Frames 48
POJOs 49
Model Files 51
Save All Models 51
Summary 52
3. The Data Sets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
Data Set: Building Energy Efficiency 54
Setup and Load 55
The Data Columns 56
Splitting the Data 58
Let’s Take a Look! 59
About the Data Set 64
Data Set: Handwritten Digits 64
Setup and Load 65
Taking a Look 67
Helping the Models 69
About the Data Set 71
Data Set: Football Scores 71
Correlations 75
Missing Data… And Yet More Columns 80
How to Train and Test? 80
Setup and Load 81
The Other Third 82
Missing Data (Again) 85
Setup and Load (Again) 86
About the Data Set 89
Summary 89
4. Common Model Parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
Supported Metrics 92
iv | Table of Contents
Regression Metrics 92
Classification Metrics 93
Binomial Classification 94
The Essentials 96
Effort 98
Scoring and Validation 99
Early Stopping 99
Checkpoints 102
Cross-Validation (aka k-folds) 104
Data Weighting 106
Sampling, Generalizing 108
Regression 110
Output Control 112
Summary 113
5. Random Forest. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
Decision Trees 116
Random Forest 116
Parameters 117
Building Energy Efficiency: Default Random Forest 119
Grid Search 122
Cartesian 123
RandomDiscrete 126
High-Level Strategy 129
Building Energy Efficiency: Tuned Random Forest 129
MNIST: Default Random Forest 132
MNIST: Tuned Random Forest 134
Enhanced Data 137
Football: Default Random Forest 138
Football: Tuned Random Forest 140
Summary 143
6. Gradient Boosting Machines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
Boosting 145
The Good, the Bad, and… the Mysterious 147
Parameters 148
Building Energy Efficiency: Default GBM 150
Building Energy Efficiency: Tuned GBM 151
MNIST: Default GBM 155
MNIST: Tuned GBM 157
Football: Default GBM 160
Football: Tuned GBM 160
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Summary 163
7. Linear Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
GLM Parameters 166
Building Energy Efficiency: Default GLM 171
Building Energy Efficiency: Tuned GLM 173
MNIST: Default GLM 179
MNIST: Tuned GLM 181
Football: Default GLM 183
Football: Tuned GLM 185
Summary 186
8. Deep Learning (Neural Nets). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
What Are Neural Nets? 188
Numbers Versus Categories 190
Network Layers 191
Activation Functions 193
Parameters 195
Deep Learning Regularization 195
Deep Learning Scoring 196
Building Energy Efficiency: Default Deep Learning 199
Building Energy Efficiency: Tuned Deep Learning 201
MNIST: Default Deep Learning 207
MNIST: Tuned Deep Learning 209
Football: Default Deep Learning 214
Football: Tuned Deep Learning 215
Summary 221
Appendix: More Deep Learning Parameters 222
9. Unsupervised Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225
K-Means Clustering 226
Deep Learning Auto-Encoder 229
Stacked Auto-Encoder 232
Principal Component Analysis 234
GLRM 236
Missing Data 237
GLRM 241
Lose the R! 241
Summary 245
10. Everything Else. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247
Staying on Top of and Poking into Things 247
vi | Table of Contents
Installing the Latest Version 247
Building from Source 248
Running from the Command Line 248
Clusters 249
EC2 250
Other Cloud Providers 251
Hadoop 251
Spark / Sparkling Water 252
Naive Bayes 252
Ensembles 253
Stacking: h2o.ensemble 254
Categorical Ensembles 256
Summary 257
11. Epilogue: Didn’t They All Do Well!. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259
Building Energy Results 259
MNIST Results 261
Football Data 263
How Low Can You Go? 265
The More the Merrier 265
Still Desperate for More 267
Filtering for Hardness 268
Auto-Encoder 269
Convolute and Shrink 270
Ensembles 272
That Was as Low as I Go… 273
Summary 273
Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275
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