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Getting started

Here are some helpful links to help you get started learning H2O-3 Secure.

Download H2O-3 Secure​

To begin, contact enterprise@h2o.ai for the download link. Make sure to install Java if it isn't already installed.

note

By default, this setup is open. Follow security guidelines if you want to secure your installation.

Launch from the command line​

You can configure H2O-3 Secure when you launch it from the command line. For example, you could allocate more memory, or you could use a flatfile for a quick configuration of your cluster. See more details about configuring the additional options when you launch H2O-3 Secure.

Algorithms​

This section describes the science behind our algorithms and provides a detailed, per-algorithm view of each model type.

Use cases​

H2O-3 Secure can handle a wide variety of practical use cases due to its robust catalogue of supported algorithms, wrappers, and machine learning tools. The following are some example problems H2O-3 Secure can handle:

  • Determining outliers in housing prices based on number of bedrooms, number of bathrooms, access to waterfront, etc. through anomaly detection.
  • Revealing natural customer segments in retail data to determine which groups are purchasing which products.
  • Linking multiple records to the same person with probabilistic matching.
  • Upsampling the minority class for credit card fraud data to handle imbalanced data.
  • Detecting drift on avocado sales pre-2018 and 2018+ to determine if a model is still relevant for new data.

See our best practice tutorials to further explore the capabilities of H2O-3 Secure.

New user quickstart​

You can follow these steps to quickly get up and running with H2O-3 OSS directly from the H2O-3 OSS repository. These steps will guide you through cloning the repository, starting H2O-3 OSS, and importing a dataset. Once you're up and running, you'll be better able to follow examples included within this user guide.

  1. In a terminal window, create a folder for the H2O-3 OSS repository:
user$ mkdir ~/Desktop/repos
  1. Change directories to that new folder, and then clone the repository. Notice that the prompt changes when you change directories:
user$ cd ~/Desktop/repos
repos user$ git clone https://github.com/h2oai/h2o-3.git
  1. After the repository is cloned, change directories to the h2o-3 folder:
repos user$ cd h2o-3
h2o-3 user$
  1. Run the following command to retrieve sample datasets. These datasets are used throughout the user guide and within the booklets.
h2o-3 user$ ./gradlew syncSmalldata

At this point, choose whether you want to complete this quickstart in Python or R. Then, run the following corresponding commands from either the Python or R tab:

# By default, this setup is open. 
# Follow the security guidelines in this documentation
# if you want to secure your installation.

# Before starting Python, run the following commands to install dependencies.
# Prepend these commands with `sudo` only if necessary:
# h2o-3 user$ [sudo] pip install -U requests
# h2o-3 user$ [sudo] pip install -U tabulate

# Start python:
# h2o-3 user$ python

# Run the following commands to import the H2O module:
>>> import h2o

# Run the following command to initialize H2O on your local machine (single-node cluster):
>>> h2o.init()

# If desired, run the GLM, GBM, or Deep Learning demo(s):
>>> h2o.demo("glm")
>>> h2o.demo("gbm")
>>> h2o.demo("deeplearning")

# Import the Iris (with headers) dataset:
>>> path = "https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_wheader.csv"
>>> iris = h2o.import_file(path=path)

# View a summary of the imported dataset:
>>> iris.summary
# sepal_len    sepal_wid    petal_len    petal_wid    class
# 5.1          3.5          1.4          0.2          Iris-setosa
# 4.9          3            1.4          0.2          Iris-setosa
# 4.7          3.2          1.3          0.2          Iris-setosa
# 4.6          3.1          1.5          0.2          Iris-setosa
# 5            3.6          1.4          0.2          Iris-setosa
# 5.4          3.9          1.7          0.4          Iris-setosa
# 4.6          3.4          1.4          0.3          Iris-setosa
# 5            3.4          1.5          0.2          Iris-setosa
# 4.4          2.9          1.4          0.2          Iris-setosa
# 4.9          3.1          1.5          0.1          Iris-setosa
#
# [150 rows x 5 columns]
# <bound method H2OFrame.summary of >
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