Workshop example adopted from official KNIME "Simple Model Training for Classification" example

Workshop example adopted from official KNIME "Simple Model Training for Classification" example

1. Run the flow - go through all data science steps
2. Prepare for advanced data science - clear the flow from text, data exploration and visualisation
3. Add models and choose the best performing one (AUC as primary metric) - note example performance and leave the best one
a. adapt partitioning and preditor - stratified, seed, individual probabilities
b. add xgboost and javascript views extensions
4. Expand flow with feature optimisation loop - simple forward feature selection
5. Expand flow with hyperparameter optimisation loop - simple stepwise (bruteforce) for one hyperparameter
a. add optimisation extension
6. Rebuild model and cross-validate drafted and final one.