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.
The results may be used also on Big Data clusters with the help of H2O.ai Sparkling Water (https://hub.knime.com/mlauber71/spaces/Public/latest/kn_example_h2o_sparkling_water). Also shown in the s_620 node in this collection
https://hub.knime.com/mlauber71/spaces/Public/latest/kn_example_bigdata_h2o_automl_spark_46
To use this workflow in KNIME, download it from the below URL and open it in KNIME:
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