JKI_Challenge-13_diy
Solution to JKI Challenge 13. This workflow runs an AutoML node and subsequently carries out a Feature Importance to identify the most important components as well as establishes the movement of the target MEDV value in comparison to the Feature attributes utilising the SHAP loop methods.
Nodes
Extensions
- FeatureKNIME Base nodes
- FeatureKNIME Deep Learning - Keras Integration
- FeatureKNIME Ensemble Learning Wrappers
- FeatureKNIME H2O Machine Learning Integration
- FeatureKNIME Integrated Deployment
- FeatureKNIME JavaScript Views
- FeatureKNIME Javasnippet
- FeatureKNIME Machine Learning Interpretability Extension
- FeatureKNIME Math Expression (JEP)
- FeatureKNIME Optimization extension
- FeatureKNIME PMML Preprocessing Applier Nodes
- FeatureKNIME Quick Forms
- FeatureKNIME Views
- FeatureKNIME XGBoost Integration
Links
- No links available