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>

man/run_imbalance_tournament.Rd

@@ -0,0 +1,34 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/functions.R
+\name{run_imbalance_tournament}
+\alias{run_imbalance_tournament}
+\title{Run Class Imbalance Tournament}
+\usage{
+run_imbalance_tournament(
+  tasks,
+  windows,
+  feature_prefix,
+  bucket_name = "baf-fraud",
+  inputs_prefix = "05_model_input"
+)
+}
+\arguments{
+\item{tasks}{A tibble containing recipe_name, data_folder, and scale_pos_weight.}
+
+\item{windows}{A tibble containing window_id, train_months, and test_month.}
+
+\item{feature_prefix}{Character. The upstream dependency prefix (used to force DAG execution).}
+
+\item{bucket_name}{Character. Bucket name. Default "baf-fraud".}
+
+\item{inputs_prefix}{Character. The folder containing the sampled data. Default "05_model_input".}
+}
+\value{
+A tibble with the summarized tournament results.
+}
+\description{
+Trains LightGBM models across different class imbalance strategies
+(Standard, SMOTE, Adasyn, etc.) using sliding time windows. Evaluates
+performance using PR-AUC and calculates statistical significance.
+Includes common-sense hyperparameter defaults to prevent overfitting.
+}