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.

External resources

• KNIME Integrated Deployment - KNIME.com
• Molnar, Christoph. "Interpretable machine learning. A Guide for Making Black Box Models Explainable", 2019.
• Give Me Some Credit - Kaggle data set