This application is a simple example of Xgboost model with KNIME Software for binary and multiclass classification. The model output is then explained via the interactive XAI View. 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.
Used extensions & nodes
Created with KNIME Analytics Platform version 4.6.1
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