Heart Disease - Machine Learning Case - LightGBM Hyper Parameter Optimization

use KNIME / Python and LightGBM to build a model - Hyperparameter tuning with BayesSearchCV and Optuna - also preparing data with vtreat

use KNIME / Python and LightGBM to build a model - Hyperparameter tuning with BayesSearchCV and Optuna - also preparing data with vtreat
some parameters have been discussed with ChatGPT ...

MEDIUM Blog: Hyperparameter optimization for LightGBM — wrapped in KNIME nodes
https://medium.com/p/ddb7ae1d7e2

GitHub Repository - binary classification
https://github.com/ml-score/knime_meets_python/tree/main/machine_learning/binary

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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
• GitHub Repository - binary classification
• MEDIUM: Hyperparameter optimization for LightGBM — wrapped in KNIME nodes
• ChatGPT and KNIME on LinkedIn
• HUB: use KNIME / Python and LightGBM to build a model - also preparing data with vtreat
• sklearn.ensemble.ExtraTreesClassifier
• Data preparation for Machine Learning with KNIME and the Python “vtreat” package