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.


This is a companion discussion topic for the original entry at https://kni.me/w/0sCM4ck-ujkML_P1
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