bank-fraud-baf-lakehouse

4 Commits 1 Branch 0 Tags

Author	SHA1	Message	Date
Rob Wiederstein	e8d2c69f2d	Remove generated report artifacts from version control Add reports/figures/, reports/slides/, reports/tables/ to .gitignore and untrack previously committed PNGs. These are build artifacts regenerated by tar_make() and deploy.R. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-02-22 03:54:51 -05:00
Rob Wiederstein	b38892f49e	Refactor: consistent naming across functions, targets, and pkgdown Functions: prepare_eda_recipe -> build_eda_recipe, create_efficiency_plot -> plot_efficiency, format_class_imbalance_tourney_gt -> format_tournament_gt Targets: model_inputs_prefix -> baf_model_input_prefix, tbl_fraud_by_month_data -> fraud_by_month_summary, model_diag -> diag_fit, winning_params -> best_params, production_recipe_blueprint -> prod_recipe, final_eval_data -> test_predictions pkgdown: restructured reference index into 6 logical sections, removed stale names and development comments. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-02-22 03:52:34 -05:00
Rob Wiederstein	f47b2e1be2	Add tune_lgbm() and wire hyperparameter tuning into DAG 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>	2026-02-22 03:25:35 -05:00
Rob Wiederstein	33d0fc31c7	Initial commit: BAF Lakehouse fraud detection pipeline 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>	2026-02-21 21:19:09 -05:00