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>
28 lines
864 B
R
28 lines
864 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{generate_model_inputs}
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\alias{generate_model_inputs}
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\title{Generate Resampled Model Inputs}
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\usage{
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generate_model_inputs(
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feature_prefix = "04_feature/variant=Base",
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out_prefix = "05_model_input",
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bucket_name = "baf-fraud"
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)
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}
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\arguments{
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\item{feature_prefix}{Character. Input prefix (e.g., "04_feature/variant=Base").}
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\item{out_prefix}{Character. Output prefix base (e.g., "05_model_input").}
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\item{bucket_name}{Character. Bucket name. Default "baf-fraud".}
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}
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\value{
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Character. The output prefix (for targets dependency tracking).
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}
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\description{
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Reads the engineered feature layer, prepares a base tidymodels recipe,
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and generates resampled datasets (Baseline, Under, SMOTE, Adasyn, Tomek)
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across all months, saving them to the 05_model_input prefix.
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}
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