@knimebine007 I toyed around with KNIME and Python and you could configure KNIME nodes that would use the XGBoost Python package and store the model in a local JSON file and reuse it with new data. The work will be done in Python but KNIME can create the workflow around.
From what I see is XGBoost still under development and some settings in the cooperation between KNIME and Python and XGB are maybe a bit strange but they will work and the jobs can be repeated.
The label is the column “Target” (0/1) and there is a “row_id” present to stand for a possible customer number or ID to later bring the scores to another system. Some XGBoost parameters might have to be adapted.
In the subfolder /data/ there is a Jupyter notebook “kn_example_python_xgboost.ipynb” where I tried to set up a running workflow. Data can be interchanged between Jupyter and KNIME. And notebooks might also be run from within KNIME.
Yes indeed the whole thing might be somewhat over-engineered but I always wanted to set up such a workflow - in general you will be best served with just using the great KNIME XGBoost integration 