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arXiv Machine Learning By Mikhail Liashkov, Ilyas Varshavskiy, Shuhratjon Khalilbekov, Azizjon Azimi, Bonu Boboeva

PaMIR: Open Benchmark of Public Credit-Default Datasets

Read the original on arXiv Machine Learning →

PaMIR (Public Arrival-ordered Measurement for Inference in Risk) is an open benchmark for credit‑default prediction that addresses the challenge of scarce and late‑arriving labels. It aggregates 19 public datasets—totaling 1.24 million loans, firms, and card accounts from nine countries—into a single, leakage‑audited recipe that is never redistributed. Models are evaluated via repeated i.i.d. splits and a label‑delayed stream, with AUC reported by label budget, and a synthetic‑data harness tests training rows without exposing held‑out data.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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