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
- How to Develop Your First XGBoost Model in Python
- A Beginner’s guide to XGBoost
- XGBoost Parameters
- forum entry (45057)
- Meta Collection about KNIME and Python
- Medium: Data preparation for Machine Learning with KNIME and the Python “vtreat” package
- H2O.ai AutoML (wrapped with Python) in KNIME for classification problems
- HUB: Binary Classification - Heart Disease - Machine Learning Case - Comparing Algorithms
- forum entry (77228)
- Medium Blog: KNIME — Machine Learning and Artificial Intelligence— A Collection
- Medium Blog: About Machine-Learning — How it Fails and Succeeds
- Medium Blog: KNIME, XGBoost and Optuna for Hyper Parameter Optimization
Used extensions & nodes
Created with KNIME Analytics Platform version 5.2.3
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KNIME H2O Machine Learning Integration - MOJO Extension
KNIME AG, Zurich, Switzerland
Version 5.2.0
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