@rogerius1st I see these approaches (besides it would be better to open a new thread and leave such old ones alone).
First- AutoML Regression and Classification Examples. Like @Daniel_Weikert has mentioned these are the components currently being developed by KNIME:
AutoML Regression and Classification Examples – KNIME Community Hub (example on the hub for classification and regression)
Guided Automation – KNIME Community Hub (guided automation)
Compute and Visualize Global Feature Importance Metrics – KNIME Community Hub (global feature importance)
There is a blog collection describing the approach:
Second - H2O.ai AutoML in KNIME for classification problems (my own little approach utilising H2O.ai AuoML)
H2O.ai AutoML (generic KNIME nodes) in KNIME for classification problems - a powerful auto-machine-learning framework
Sparkling predictions and encoded labels - “the poor man’s ML Ops”
Results get evaluated with R node collection (Model Quality Classification - Graphics – KNIME Community Hub) and are stored in sub-folders
Third - The KNIME Model Process Factory (2017) - an older approach by @Iris at collecting and evaluating models