H2O.ai AutoML (generic KNIME nodes) in KNIME for classification problems - a powerful auto-machine-learning framework

H2O.ai AutoML (generic KNIME nodes) in KNIME for classification problems - a powerful auto-machine-learning framework (https://hub.knime.com/mlauber71/spaces/Public/latest/automl/)
v 1.75

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. Please refer to the green box on the right.

External resources

• Overview of KNIME ML and AutoML frameworks
• Cohen's Kappa: what it is, when to use it, how to avoid pitfalls
• Machine Learning Meta Collection (with KNIME)
• paolotamag - more options for AutoML with KNIME components
• H2O.ai AutoML in KNIME for classification problems
• KNIME Interactive R Statistics Integration Installation Guide
• Combine Big Data, Spark and H2O.ai Sparkling Water
• A Deep dive into H2O’s AutoML
• Profile mlauber71
• 11 Important Model Evaluation Metrics for Machine Learning Everyone should know
• Downloading & Installing H2O