Describing the KNIME H2O Machine Learning Integration
The KNIME H2O Machine Learning Integration enables fast and scalable execution of machine learning tasks from within your KNIME workflows. When you execute tasks on H2O, you will build your workflows in much the same way as before – codeless – yet under the hood, the tasks are performed on H2O data frames in a cluster instance and processed via distributed in-memory computing. The H2O data frame is the main data structure for H2O, with numbered rows and named columns, located in an H2O cluster.
We will introduce the setup and functionalities of the H2O integration in the following subsections:
- Starting a workflow running on the H2O platform
- Introducing the H2O nodes for machine learning
In the first subsection, we show you how to get started with H2O workflows in KNIME.
Starting a workflow running on the H2O platform
Building H2O workflows requires the extension called the KNIME H2O Machine...
