03_Global_Feature_Importance
Global Feature Importance Component with AutoML
In the example, the Credit Scoring data set is partitioned to training and test samples. Then, the black box model (Neural Network) is trained on the standardly pre-processed training data using the AutoML component. The Workflow Object capturing the pre-processing and the model is provided as one of the inputs for the Global Feature Importance component.
The Global Feature Importance component is then used to inspect the global model behavior using three Global Surrogate models (Generalized Linear Model, Decision Tree, and Random Forest) and Permutation Feature Importance explainability technique.
Nodes
Extensions
- FeatureKNIME Base nodes
- FeatureKNIME Data Generation
- 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