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>
35 lines
1000 B
R
35 lines
1000 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{plot_fraud_by_month}
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\alias{plot_fraud_by_month}
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\title{Plot applications by month (Legit vs Fraud) on a log scale}
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\usage{
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plot_fraud_by_month(
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dataset_prefix,
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bucket_name = "baf-fraud",
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palette = "Dark 3",
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title = ""
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)
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}
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\arguments{
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\item{dataset_prefix}{Character. Prefix inside the bucket, e.g. "03_primary/variant=Base".}
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\item{bucket_name}{Character. Bucket name. Default "baf-fraud".}
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\item{palette}{Character. colorspace qualitative palette name. Default "Dark 3".}
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\item{title}{Character. Plot title. Default "".}
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}
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\value{
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A ggplot object.
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}
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\description{
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Builds an exploratory chart of absolute application counts by month
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split by outcome (Legit vs Fraud). Uses a log10 y-axis so rare fraud
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remains visible on the same axis.
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}
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\details{
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Data source: expects a cleaned "primary" dataset prefix (e.g. 03_primary/variant=Base)
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stored in MinIO/S3, accessed via \code{connect_baf()}.
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}
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