Converts scratch/tune_model.R into a pure tune_lgbm() function,
replacing hardcoded winning_params with a fully automated tar_target.
Best params (trees=844, depth=3, lr=0.0204, min_n=389) now flow
reproducibly into evaluate_final_model() and train_production_model().
PR-AUC improved from 0.165 to 0.198.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
End-to-end LightGBM fraud detection pipeline built as an R package,
orchestrated by targets with data stored in MinIO via Apache Arrow.
Includes 6-layer Lakehouse architecture, class imbalance tournament,
formally tuned hyperparameters (PR-AUC 0.198), and Quarto RevealJS slides.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
