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fraud-detection / models

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History: 2 commits

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2026-06-05: audit fixes — directed GNN edges, realistic cost model, 85% stream catch, cross-dataset tab

d724279 verified 1 day ago

autoencoder_meta.json

497 Bytes
2026-06-05: deploy fraud detection dashboard (LightGBM + GNN + autoencoder, SHAP, drift, live scoring) 1 day ago
cross_dataset_ulb.json

1.46 kB
2026-06-05: audit fixes — directed GNN edges, realistic cost model, 85% stream catch, cross-dataset tab 1 day ago
drift_report.json

2.22 kB
2026-06-05: deploy fraud detection dashboard (LightGBM + GNN + autoencoder, SHAP, drift, live scoring) 1 day ago
gnn_meta.json

509 Bytes
2026-06-05: audit fixes — directed GNN edges, realistic cost model, 85% stream catch, cross-dataset tab 1 day ago
lgbm_fraud.joblib
Detected Pickle imports (8)
- "lightgbm.basic.Booster",
- "numpy.ndarray",
- "collections.OrderedDict",
- "numpy.dtype",
- "numpy.core.multiarray.scalar",
- "lightgbm.sklearn.LGBMClassifier",
- "collections.defaultdict",
- "joblib.numpy_pickle.NumpyArrayWrapper"
How to fix it?
4.44 MB
xet

2026-06-05: deploy fraud detection dashboard (LightGBM + GNN + autoencoder, SHAP, drift, live scoring) 1 day ago
model_meta.json

17.2 kB
2026-06-05: audit fixes — directed GNN edges, realistic cost model, 85% stream catch, cross-dataset tab 1 day ago
stream_benchmark.json

261 Bytes
2026-06-05: audit fixes — directed GNN edges, realistic cost model, 85% stream catch, cross-dataset tab 1 day ago