Fast Scalable R with H2O
Fast Scalable R with H2O
About the Book
About the Author
Table of Contents
- Copyright Information
- What is H2O?
- Introduction
-
Installation
- Installing R or R Studio
- Installing H2O in R
- Making a Build from the Source Code
-
H2O Initialization
- Launching from R
- Launching from the Command Line
- Launching on Hadoop
- Launching on an EC2
- Checking Cluster Status
-
Data Preparation in R
- Notes
- Tools and Methods
- Creating Aggregates from Split Data
-
Models
- Demo: GLM
-
Data Manipulation in R
- Importing Files
- Uploading Files
- Finding Factors
- Converting to Factors
- Converting Data Frames
- Transferring Data Frames
- Renaming Data Frames
- Getting Column Names
- Getting Minimum and Maximum Values
- Getting Quantiles
- Summarizing Data
- Summarizing Data in a Table
- Generating Random Uniformly Distributed Numbers
- Splitting Frames
- Getting Frames
- Getting Models
- Listing H2O Objects
- Removing H2O Objects
- Adding Functions
-
Running Models
- Gradient Boosted Models
- Generalized Linear Models
- K Means
- Principal Components Analysis (PCA)
- Principal Components Regression (PCR)
- Predictions
- Support
- References
-
Appendix: Commands
- Data Set Operations
- General Data Operations
- Methods from Group Generics
- Other Aggregations
- Data Munging
- Data Modeling
- H2O Cluster Operations
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