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