Workflow
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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The data used has beed adapted from:
Census Income Data Set
Abstract: Predict whether income exceeds $50K/yr based on census data. Also known as "Adult" dataset.
Extract and prepare the Census Income Files for usage in KNIME
https://archive.ics.uci.edu/ml/datasets/census+income
External resources
- 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
Used extensions & nodes
Created with KNIME Analytics Platform version 4.7.7
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KNIME H2O Machine Learning Integration - MOJO Extension
KNIME AG, Zurich, Switzerland
Version 4.7.0
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