02_​Explainable_​Artificial_​Intelligence_​(XAI)

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This application is a simple example of AutoML with KNIME Software for binary and multiclass classification. The output models are then explained via the interactive XAI View, which works for any model the AutoML component produces. Machine Learning Interpretability (MLI) techniques used: SHAP explanations/reason codes, partial dependence, individual conditional expectation (ICE) curves and a surrogate decision tree.

The workflow also works locally on KNIME Analytics Platform. Make sure to use "Apply and Close" in bottom-right corner of each view.

URL: KNIME Integrated Deployment - KNIME.com https://www.knime.com/integrated-deployment
URL: AutoML Component - KNIME Hub https://kni.me/c/33fQGaQzuZByy6hE
URL: XAI View Component - KNIME Hub https://kni.me/c/no0cArkQ94BnNpeb
URL: Interpretable Machine Learning - Christoph Molnar - 2020-09-28 https://christophm.github.io/interpretable-ml-book/
URL: Explainable artificial intelligence - WIkipedia https://en.wikipedia.org/wiki/Explainable_artificial_intelligence

Nodes

• Column Filter118 ×
• Component Input76 ×
• Component Output76 ×
• Rule Engine Variable76 ×
• Column Rename70 ×
• Show all 146 nodes

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Data Generation
• FeatureKNIME Deep Learning - Keras Integration
• FeatureKNIME Ensemble Learning Wrappers
• FeatureKNIME H2O Machine Learning Integration
• Show all 14 modules

Links

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