Heart Disease - Machine Learning Case - Comparing Algorithms

Binary Classification - use Python XGBoost package and other nodes to build model and deploy that thru KNIME Python nodes

prepare data with vtreat package
in the subfolder /data/notebooks/ there is a Jupyter notebook to experiment and build XGBoost models ("kn_example_python_xgboost.ipynb")

Also you can further explore the H2O.ai AutoML model with the notebook "h2o_inspect_model_automl_existing.ipynb"

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Heart Failure Prediction Dataset (Kaggle)
11 clinical features for predicting heart disease events.

https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction?resource=download

External resources

• Medium Blog: KNIME, XGBoost and Optuna for Hyper Parameter Optimization
• Medium Blog: About Machine-Learning — How it Fails and Succeeds
• Medium Blog: KNIME — Machine Learning and Artificial Intelligence— A Collection
• forum entry (77228)
• HUB: Binary Classification - Heart Disease - Machine Learning Case - Comparing Algorithms
• H2O.ai AutoML (wrapped with Python) in KNIME for classification problems
• Medium: Data preparation for Machine Learning with KNIME and the Python “vtreat” package
• Meta Collection about KNIME and Python
• forum entry (45057)
• XGBoost Parameters
• A Beginner’s guide to XGBoost
• How to Develop Your First XGBoost Model in Python