Overview

Welcome to the H2O documentation site! Depending on your area of interest, select a learning path from the sidebar, or look at the full content outline below.

We’re glad you’re interested in learning more about H2O. If you have questions or ideas to share, please post them to the H2O community site on Stack Overflow.

• Welcome to H2O 3
  • Requirements
  • New Users
  • Experienced Users
  • Flow Users
  • Sparkling Water Users
  • Python Users
  • R Users
  • API Users
  • Java Users
  • Developers
  • Hadoop Users
  • Docker Users

• Quick Start Videos
  • H2O Quick Start with Flow
  • H2O Quick Start with Python
  • H2O Quick Start on Hadoop
  • H2O Quick Start with Sparkling Water
  • H2O Quick Start with R

• Cloud Integration
  • EC2 Instances & S3 Storage
  • Using H2O with Amazon AWS
  • Using H2O with Microsoft Azure HDInsight - Beta
  • Using H2O with Microsoft Azure Linux Data Science VM
  • Using H2O Sparkling Water with Databricks
  • Using H2O with IBM Data Science Experience
  • Using H2O with Nimbix
  • Using H2O with Google Cloud Storage
  • Install H2O on the Google Cloud Platform Offering

• Downloading & Installing H2O
  • Download and Run
  • Install in R
  • Install in Python
  • Install on Hadoop

• Starting H2O
  • From R
  • From Python
  • From the Command Line
  • On Spark

• Getting Data into Your H2O Cluster
  • Supported File Formats
  • Data Sources

• Data Manipulation
  • Uploading a File
  • Importing a File
  • Importing Multiple Files
  • Combining Columns from Two Datasets
  • Combining Rows from Two Datasets
  • Fill NAs
  • Group By
  • Imputing Data
  • Merging Two Datasets
  • Pivoting Tables
  • Replacing Values in a Frame
  • Slicing Columns
  • Slicing Rows
  • Sorting Columns
  • Splitting Datasets into Training/Testing/Validating
  • Target Encoding

• Algorithms
  • Common
  • Supervised
  • Unsupervised
  • Miscellaneous

• Cross-Validation
  • How Cross-Validation is Calculated
  • Using Cross-Validated Predictions
  • Combining Holdout Predictions

• Grid (Hyperparameter) Search
  • Quick Links
  • Grid Search in R
  • Grid Search in Python
  • Grid Search Java API
  • REST API
  • Supported Grid Search Hyperparameters
  • Grid Testing
  • Caveats/In Progress
  • Additional Documentation

• Checkpointing Models
  • Checkpoint with Deep Learning
  • Checkpoint with DRF
  • Checkpoint with GBM

• AutoML: Automatic Machine Learning
  • AutoML Interface
  • AutoML Output

• Saving and Loading a Model
  • In R and Python
  • In Flow

• Productionizing H2O
  • About POJOs and MOJOs
  • Example Design Patterns
  • Additional Resources

• Using Flow - H2O’s Web UI
  • About
  • Download Flow
  • Launch Flow
  • How to Use the Interface
  • Using Flows
  • Understanding Cell Modes
  • Data
  • Viewing Jobs
  • Models
  • Partial Dependence Plots
  • Predictions
  • Frames
  • Troubleshooting Flow

• Downloading Logs
  • For Non-Hadoop Users
  • Hadoop Users
  • Accessing YARN

• H2O Architecture
  • H2O Software Stack
  • REST API Clients
  • How R (and Python) Interacts with H2O

• Security
  • Security Model
  • File Security in H2O
  • Embedded Web Port (by default port 54321) Security
  • SSL Internode Security

• FAQ

• Glossary

• Migrating to H2O 3

• Appendix A - Parameters

• Appendix B - API Reference