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>
38 lines
1.2 KiB
R
38 lines
1.2 KiB
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{engineer_features}
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\alias{engineer_features}
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\title{Engineer features for the BAF dataset}
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\usage{
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engineer_features(
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in_prefix = "03_primary/variant=Base",
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out_prefix = "04_feature/variant=Base",
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bucket_name = "baf-fraud",
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partitioning = "month",
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existing_data_behavior = "delete_matching",
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verbose = TRUE
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)
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}
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\arguments{
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\item{in_prefix}{Character. Input dataset prefix (e.g., "03_primary/variant=Base").}
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\item{out_prefix}{Character. Output dataset prefix (e.g., "04_feature/variant=Base").}
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\item{bucket_name}{Character. The S3/MinIO bucket name. Default "baf-fraud".}
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\item{partitioning}{Character vector. Columns to partition by. Default "month".}
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\item{existing_data_behavior}{Character. Behavior when data exists. Default "delete_matching".}
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\item{verbose}{Logical. Whether to print progress messages. Default TRUE.}
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}
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\value{
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Character. The output prefix path for downstream targets.
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}
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\description{
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Reads the primary BAF dataset and engineers new features, such as
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\code{n_missing}, which counts the number of missing values across key
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tenure and financial columns. This calculation is performed out-of-memory
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using Arrow compute.
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}
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