% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functions.R
\name{tune_lgbm}
\alias{tune_lgbm}
\title{Tune LightGBM Hyperparameters}
\usage{
tune_lgbm(
  imbalance_windows,
  bucket_name = "baf-fraud",
  inputs_prefix = "05_model_input",
  grid_size = 30L,
  seed = 42L
)
}
\arguments{
\item{imbalance_windows}{A tibble with columns \code{window_id},
\code{train_months}, and \code{test_month}, as produced by the
\code{imbalance_windows} target.}

\item{bucket_name}{Character. MinIO bucket name. Default \code{"baf-fraud"}.}

\item{inputs_prefix}{Character. Prefix for the model input layer.
Default \code{"05_model_input"}.}

\item{grid_size}{Integer. Number of space-filling candidates. Default \code{30}.}

\item{seed}{Integer. Random seed for reproducibility. Default \code{42}.}
}
\value{
A named list with elements \code{trees}, \code{tree_depth},
\code{learn_rate}, and \code{min_n}.
}
\description{
Performs a grid search over LightGBM hyperparameters using the same rolling
time windows as the imbalance tournament. Optimises PR-AUC on the pre-baked
baseline data stored in MinIO. Returns the best parameters as a named list
ready for use in \code{evaluate_final_model()} and
\code{train_production_model()}.
}