{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:OCDL3T6Y222LHNMEXE5NOX62AR","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"1d10ec2be4b800a476eb717255fba2e419f836f0ea4ca456d35764ec88349c5f","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2025-03-27T09:59:45Z","title_canon_sha256":"4e55ad577004edee15cf90b51f3a84b68e54fe5faa71676c59e5d0d3b315a13f"},"schema_version":"1.0","source":{"id":"2503.21321","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.21321","created_at":"2026-06-23T03:13:44Z"},{"alias_kind":"arxiv_version","alias_value":"2503.21321v2","created_at":"2026-06-23T03:13:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.21321","created_at":"2026-06-23T03:13:44Z"},{"alias_kind":"pith_short_12","alias_value":"OCDL3T6Y222L","created_at":"2026-06-23T03:13:44Z"},{"alias_kind":"pith_short_16","alias_value":"OCDL3T6Y222LHNME","created_at":"2026-06-23T03:13:44Z"},{"alias_kind":"pith_short_8","alias_value":"OCDL3T6Y","created_at":"2026-06-23T03:13:44Z"}],"graph_snapshots":[{"event_id":"sha256:8de7c40357c6049425eb2e26f9b110aa92e8c306d439155113c11efc71a0366f","target":"graph","created_at":"2026-06-23T03:13:44Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2503.21321/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"With the rapid development of machine learning and deep learning techniques, actuaries and the broader insurance industry face a persistent trade-off between predictive accuracy and interpretability. This paper provides a comprehensive applied assessment of Explainable Boosting Machines (EBM) in a car insurance framework, focusing on claim frequency and severity modeling. EBM combines the additive structure of generalized additive models (GAM) with a cyclic gradient boosting algorithm, resulting in a glass-box model whose predictions are interpretable by design. Using real-world data, we empir","authors_text":"Mark\\'eta Kr\\'upov\\'a, Nabil Rachdi, Quentin Guibert (CEREMADE)","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2025-03-27T09:59:45Z","title":"Explainable Boosting Machine for Predicting Claim Severity and Frequency in Car Insurance"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.21321","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:62ca0024d071e6ae523cd360e617e1695173344096654f18cceafec1a21bf6ac","target":"record","created_at":"2026-06-23T03:13:44Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"1d10ec2be4b800a476eb717255fba2e419f836f0ea4ca456d35764ec88349c5f","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2025-03-27T09:59:45Z","title_canon_sha256":"4e55ad577004edee15cf90b51f3a84b68e54fe5faa71676c59e5d0d3b315a13f"},"schema_version":"1.0","source":{"id":"2503.21321","kind":"arxiv","version":2}},"canonical_sha256":"7086bdcfd8d6b4b3b584b93ad75fda046d641ca909a41fdb6ff61d0dd5ca50e0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7086bdcfd8d6b4b3b584b93ad75fda046d641ca909a41fdb6ff61d0dd5ca50e0","first_computed_at":"2026-06-23T03:13:44.819496Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-23T03:13:44.819496Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"a0cQypGf8UlyZqOYa3p66LtYQTQuRrKPOYRPQPp+h8zISofMx4i2Emmc3NSxzLCdqm3FHeZH+TlhRE/KowC6BQ==","signature_status":"signed_v1","signed_at":"2026-06-23T03:13:44.819973Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.21321","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:62ca0024d071e6ae523cd360e617e1695173344096654f18cceafec1a21bf6ac","sha256:8de7c40357c6049425eb2e26f9b110aa92e8c306d439155113c11efc71a0366f"],"state_sha256":"e8e4889c434cc1dd6ee7176d2c6ecb12782cae617eedbdac617870c7f73c5f87"}