s_618 - H2O.ai AutoML (generic KNIME nodes) in KNIME for classification problems - a powerful auto-machine-learning framework applied via Sparkling Water on a Big Data system
It features various models like Random Forest along with Deep Learning. The results will be written to a folder and the models will be stored in MOJO format to be used in KNIME (as well as on a Big Data cluster via Sparkling Water). One major parameter to set is the running time the model has to test various models and do some hyper parameter optimization as well. The best model of each round is stored and some graphics are produced to see the results.
To run the validations in this workflow you have to install R with several packages or use the Conda Environment Propagation provided. Please refer to the green box on the right.
External resources
- Downloading & Installing H2O
- 11 Important Model Evaluation Metrics for Machine Learning Everyone should know
- Profile mlauber71
- A Deep dive into H2O’s AutoML
- Combine Big Data, Spark and H2O.ai Sparkling Water
- KNIME Interactive R Statistics Integration Installation Guide
- H2O.ai AutoML in KNIME for classification problems
- paolotamag - more options for AutoML with KNIME components
- Machine Learning Meta Collection (with KNIME)
- Cohen's Kappa: what it is, when to use it, how to avoid pitfalls
Used extensions & nodes
Created with KNIME Analytics Platform version 4.6.0
- Go to item
KNIME H2O Machine Learning Integration - MOJO Extension
KNIME AG, Zurich, Switzerland
Version 4.6.0
- Go to item
- Go to item
- Go to item
- Go to item
- Go to item
- Go to item
Loading deployments
Loading ad hoc executions
Legal
By using or downloading the workflow, you agree to our terms and conditions.
Discussion
Discussions are currently not available, please try again later.