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.
Workflow
Workshop example adopted from official KNIME "Simple Model Training for Classification" example
Used extensions & nodes
Created with KNIME Analytics Platform version 4.0.2
- Go to item
- Go to item
- Go to item
- Go to item
- Go to item
- Go to item
Loading deployments
Loading ad hoc executions
Legal
By using or downloading the workflow, you agree to our terms and conditions.
Discussion
Discussions are currently not available, please try again later.