{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:KDI56UNCFTIRPALCF3YFSOWKSI","short_pith_number":"pith:KDI56UNC","canonical_record":{"source":{"id":"2412.14916","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-12-19T14:50:10Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"45e86f3b169f5f1aeac404c21d764009d9677eb8745cabe9cb74789a9c3b01a1","abstract_canon_sha256":"0fb883dbe67efe91dda01e321e3b6630f3835fa4c63e51f95bd6a9ee41e1d2a6"},"schema_version":"1.0"},"canonical_sha256":"50d1df51a22cd11781622ef0593aca923f5d977e44572c4bccb5deddf12fda09","source":{"kind":"arxiv","id":"2412.14916","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.14916","created_at":"2026-07-05T11:47:59Z"},{"alias_kind":"arxiv_version","alias_value":"2412.14916v2","created_at":"2026-07-05T11:47:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.14916","created_at":"2026-07-05T11:47:59Z"},{"alias_kind":"pith_short_12","alias_value":"KDI56UNCFTIR","created_at":"2026-07-05T11:47:59Z"},{"alias_kind":"pith_short_16","alias_value":"KDI56UNCFTIRPALC","created_at":"2026-07-05T11:47:59Z"},{"alias_kind":"pith_short_8","alias_value":"KDI56UNC","created_at":"2026-07-05T11:47:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:KDI56UNCFTIRPALCF3YFSOWKSI","target":"record","payload":{"canonical_record":{"source":{"id":"2412.14916","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-12-19T14:50:10Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"45e86f3b169f5f1aeac404c21d764009d9677eb8745cabe9cb74789a9c3b01a1","abstract_canon_sha256":"0fb883dbe67efe91dda01e321e3b6630f3835fa4c63e51f95bd6a9ee41e1d2a6"},"schema_version":"1.0"},"canonical_sha256":"50d1df51a22cd11781622ef0593aca923f5d977e44572c4bccb5deddf12fda09","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:47:59.155287Z","signature_b64":"wql1uicCpBmOU4yD7CQvZWr9Z4y2ZaZ6h7vBcafNwwMttgTQ+TeMgtxh9MQAWrfGtG13GAC+BmC494ftq3OFCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"50d1df51a22cd11781622ef0593aca923f5d977e44572c4bccb5deddf12fda09","last_reissued_at":"2026-07-05T11:47:59.154758Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:47:59.154758Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.14916","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:47:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FxH9YgZIt7Phra/EmTjd+C4oyjqrcM2h2pRwx7asZLMcvWclJkuNRsBmVJSb6XAz/H5H367EGhOhG11fcby9Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T03:29:40.103871Z"},"content_sha256":"72f24a9e36b45ec0c0a470e64865320c94e25a4e42bf040af012ec981568e11e","schema_version":"1.0","event_id":"sha256:72f24a9e36b45ec0c0a470e64865320c94e25a4e42bf040af012ec981568e11e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:KDI56UNCFTIRPALCF3YFSOWKSI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"From Point to probabilistic gradient boosting for claim frequency and severity prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Dominik Chevalier, Marie-Pier C\\^ot\\'e","submitted_at":"2024-12-19T14:50:10Z","abstract_excerpt":"Gradient boosting for decision tree algorithms are increasingly used in actuarial applications as they show superior predictive performance over traditional generalised linear models. Many enhancements to the first gradient boosting machine algorithm exist. We present in a unified notation, and contrast, all the existing point and probabilistic gradient boosting for decision tree algorithms: GBM, XGBoost, DART, LightGBM, CatBoost, EGBM, PGBM, XGBoostLSS, cyclic GBM, and NGBoost. In this comprehensive numerical study, we compare their performance on five publicly available datasets for claim fr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.14916","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2412.14916/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:47:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a86rcMdfCBbxe1ryb9N/7LMd7qpmwikQL+ZiGmo9NuzOq/VZYwTuNHZCswEMyGlE1xH9Op5SMbeAq/QPgxg6BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T03:29:40.104400Z"},"content_sha256":"10e8a710ca3384fa65ba20e98d1a6bf6147d581a83edb5f87587289309715ce0","schema_version":"1.0","event_id":"sha256:10e8a710ca3384fa65ba20e98d1a6bf6147d581a83edb5f87587289309715ce0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KDI56UNCFTIRPALCF3YFSOWKSI/bundle.json","state_url":"https://pith.science/pith/KDI56UNCFTIRPALCF3YFSOWKSI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KDI56UNCFTIRPALCF3YFSOWKSI/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-12T03:29:40Z","links":{"resolver":"https://pith.science/pith/KDI56UNCFTIRPALCF3YFSOWKSI","bundle":"https://pith.science/pith/KDI56UNCFTIRPALCF3YFSOWKSI/bundle.json","state":"https://pith.science/pith/KDI56UNCFTIRPALCF3YFSOWKSI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KDI56UNCFTIRPALCF3YFSOWKSI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:KDI56UNCFTIRPALCF3YFSOWKSI","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":"0fb883dbe67efe91dda01e321e3b6630f3835fa4c63e51f95bd6a9ee41e1d2a6","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-12-19T14:50:10Z","title_canon_sha256":"45e86f3b169f5f1aeac404c21d764009d9677eb8745cabe9cb74789a9c3b01a1"},"schema_version":"1.0","source":{"id":"2412.14916","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.14916","created_at":"2026-07-05T11:47:59Z"},{"alias_kind":"arxiv_version","alias_value":"2412.14916v2","created_at":"2026-07-05T11:47:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.14916","created_at":"2026-07-05T11:47:59Z"},{"alias_kind":"pith_short_12","alias_value":"KDI56UNCFTIR","created_at":"2026-07-05T11:47:59Z"},{"alias_kind":"pith_short_16","alias_value":"KDI56UNCFTIRPALC","created_at":"2026-07-05T11:47:59Z"},{"alias_kind":"pith_short_8","alias_value":"KDI56UNC","created_at":"2026-07-05T11:47:59Z"}],"graph_snapshots":[{"event_id":"sha256:10e8a710ca3384fa65ba20e98d1a6bf6147d581a83edb5f87587289309715ce0","target":"graph","created_at":"2026-07-05T11:47:59Z","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/2412.14916/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Gradient boosting for decision tree algorithms are increasingly used in actuarial applications as they show superior predictive performance over traditional generalised linear models. Many enhancements to the first gradient boosting machine algorithm exist. We present in a unified notation, and contrast, all the existing point and probabilistic gradient boosting for decision tree algorithms: GBM, XGBoost, DART, LightGBM, CatBoost, EGBM, PGBM, XGBoostLSS, cyclic GBM, and NGBoost. In this comprehensive numerical study, we compare their performance on five publicly available datasets for claim fr","authors_text":"Dominik Chevalier, Marie-Pier C\\^ot\\'e","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-12-19T14:50:10Z","title":"From Point to probabilistic gradient boosting for claim frequency and severity prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.14916","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":"sha256:72f24a9e36b45ec0c0a470e64865320c94e25a4e42bf040af012ec981568e11e","target":"record","created_at":"2026-07-05T11:47:59Z","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":"0fb883dbe67efe91dda01e321e3b6630f3835fa4c63e51f95bd6a9ee41e1d2a6","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-12-19T14:50:10Z","title_canon_sha256":"45e86f3b169f5f1aeac404c21d764009d9677eb8745cabe9cb74789a9c3b01a1"},"schema_version":"1.0","source":{"id":"2412.14916","kind":"arxiv","version":2}},"canonical_sha256":"50d1df51a22cd11781622ef0593aca923f5d977e44572c4bccb5deddf12fda09","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"50d1df51a22cd11781622ef0593aca923f5d977e44572c4bccb5deddf12fda09","first_computed_at":"2026-07-05T11:47:59.154758Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:47:59.154758Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wql1uicCpBmOU4yD7CQvZWr9Z4y2ZaZ6h7vBcafNwwMttgTQ+TeMgtxh9MQAWrfGtG13GAC+BmC494ftq3OFCA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:47:59.155287Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.14916","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:72f24a9e36b45ec0c0a470e64865320c94e25a4e42bf040af012ec981568e11e","sha256:10e8a710ca3384fa65ba20e98d1a6bf6147d581a83edb5f87587289309715ce0"],"state_sha256":"213f8fb64bbde307af44caadc5bd879cca453af9d756e0688f7a2ea3cd141197"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"icGFIBv79HyD+B2nuOWCZU0IpZ6vswskb6MlUe4/FuMblBW1JBYw4TaFjxcDHG2nx8WS0uXsuSUC9vC3KKhZCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T03:29:40.108882Z","bundle_sha256":"8b61164450fc1a251d2ece192dd087420c732f0e0a46da4675b6ac1b7da3d994"}}