H2O.ai AutoML (wrapped with R) in KNIME for classification problems

H2O.ai AutoML (wrapped with R) 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 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 R and H2O.ai with several packages. Please refer to the green box on the right.

External resources

• 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
• Kaggle - House Prices: Advanced Regression Techniques