02_​XAI_​Credit_​Scoring_​Example

0 ×

This workflow demonstrates the usage of the verified components developed to interpret machine learning models.

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 model interpretability components.

The components allow for both global (Global Surrogates, Permutation Feature Importance, Partial Dependence Plot) and local (Counterfactual Explanations, Local Surrogates, ICE, SHAP) explainability techniques.

URL: KNIME Integrated Deployment - KNIME.com https://www.knime.com/integrated-deployment
URL: Molnar, Christoph. "Interpretable machine learning. A Guide for Making Black Box Models Explainable", 2019. https://christophm.github.io/interpretable-ml-book/
URL: Give Me Some Credit - Kaggle data set https://www.kaggle.com/c/GiveMeSomeCredit/

Nodes

• Column Filter169 ×
• Column Rename112 ×
• Rule Engine Variable110 ×
• Component Input108 ×
• Component Output108 ×
• Show all 167 nodes

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Data Generation
• FeatureKNIME Deep Learning - Keras Integration
• FeatureKNIME Distance Matrix
• FeatureKNIME Ensemble Learning Wrappers
• Show all 16 modules

Links

• No links available