Rob Wiederstein
33d0fc31c7
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>
31 lines
860 B
R
31 lines
860 B
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/functions.R
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\name{train_production_model}
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\alias{train_production_model}
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\title{Train and Serialize Production LightGBM Model}
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\usage{
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train_production_model(
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data,
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recipe,
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best_params,
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model_filename = "lgbm_prod.txt"
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)
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}
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\arguments{
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\item{data}{A data frame containing the full BAF dataset (Months 0-7).}
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\item{recipe}{A prepared tidymodels recipe.}
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\item{best_params}{A list or tibble of the winning hyperparameters.}
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\item{model_filename}{Character. The target filename. Defaults to "lgbm_prod.txt".}
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}
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\value{
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Character. The MinIO URI of the uploaded model artifact.
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}
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\description{
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Trains a LightGBM model on the complete dataset using the winning
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hyperparameters, serializes it to a text file, and uploads it directly
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to MinIO via the Apache Arrow S3 interface.
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}
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