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the LGBM looks great, and that optuna certainly seems like a clean way for model optimization i'll have to learn about that library. currently the only deployed model is the multi_model, and for it to deploy we have defined a wrapper around the model could you make something similar? |
extreme4all
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Oct 16, 2025
src/binary_model/train.py
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| model = LGBMClassifier(random_state=42, **best_params) | ||
| model.fit(X, y) | ||
| mlflow.log_params(best_params) | ||
| mlflow.sklearn.log_model(model, artifact_path="refit_model") |
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can you also add some metrics for the refit_best model
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Sounds good, I'll run some similar experiments on the multi_model and update the PR once done. |
Author
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Introduced similar changes to the multimodel. I tried to make it so the deployment pipeline would need minimal changes. Let me know if anything here would break the larger deployment pipeline. @extreme4all |
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This PR adds LightGBM + optuna optimization. It begins with an initial hyperparameter guess from long previous experiments, and does ten 5-fold stratified cross-validation experiments to do a short exploration of other hyperparameter sets. It still saves metrics and the final model artifact using mlflow.