H2O.ai AutoML (wrapped with Python) in KNIME for classification problems - a powerful auto-machine-learning framework (https://hub.knime.com/mlauber71/spaces/Public/latest/automl/)
v 1.90
It features various models like Random Forest or XGBoost along with Deep Learning. It has warppers for R and Python but also could be used from KNIME. 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 this workflow you have to install Python (https://medium.com/p/2ac217792539) and H2O.ai as well as R (https://medium.com/p/6494a2a498cc) and several packages. 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)
v 1.90
It features various models like Random Forest or XGBoost along with Deep Learning. It has warppers for R and Python but also could be used from KNIME. 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 this workflow you have to install Python (https://medium.com/p/2ac217792539) and H2O.ai as well as R (https://medium.com/p/6494a2a498cc) and several packages. 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)
