eXplainable Artificial Intelligence (XAI) - Complex

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.

This is a companion discussion topic for the original entry at https://kni.me/w/JZbuUdhGKZBEpdoK

I have downloaded the workflow and ran it step by step using the csv provided.
It produced an error on the ML component.
“Gerenarized Linear Model (H2O) Meta Learner Contains 2 unconnected nodes”
What can I do?