JKISeason2-13
Description: You ara a data scientist working for a real estate company, and heard a rumour that the "average number of rooms per dwelling" (RM) may be connected to the "per capita crime rate" (CRIM) depending on the city/town. You then decide to investigate if this is the case for Boston, the city where you live and work from. To this end, you decide to experiment with a machine learning regression model and with a topic that you have recently been studying: XAI. How are RM and CRIM connected in Boston? Hint: Consider calculating the SHAP values of each independent feature using a SHAP loop. Hint 2: Consider using a dependence plot to verify how RM and CRIM are connected visually.
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 XGBoost Integration