{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:LCVJLPKK5IXD2XHBOL2VJTC3LW","short_pith_number":"pith:LCVJLPKK","schema_version":"1.0","canonical_sha256":"58aa95bd4aea2e3d5ce172f554cc5b5d963a0d882a359752ad3c4d5fbcbff939","source":{"kind":"arxiv","id":"2305.17094","version":1},"attestation_state":"computed","paper":{"title":"Benchmarking state-of-the-art gradient boosting algorithms for classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Adam Zagda\\'nski, Piotr Florek","submitted_at":"2023-05-26T17:06:15Z","abstract_excerpt":"This work explores the use of gradient boosting in the context of classification. Four popular implementations, including original GBM algorithm and selected state-of-the-art gradient boosting frameworks (i.e. XGBoost, LightGBM and CatBoost), have been thoroughly compared on several publicly available real-world datasets of sufficient diversity. In the study, special emphasis was placed on hyperparameter optimization, specifically comparing two tuning strategies, i.e. randomized search and Bayesian optimization using the Tree-stuctured Parzen Estimator. The performance of considered methods wa"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2305.17094","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-26T17:06:15Z","cross_cats_sorted":[],"title_canon_sha256":"42de15f3a7f767a58a3b5915f5729522d903702f0da83a17a2eb0e076ac0fd23","abstract_canon_sha256":"0cbec685c2a3f9dea7850d50c8d34193d44bfc9b9b2f6494d4428803e7fffdf2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:14:21.143532Z","signature_b64":"o1O2cWADGU1ES1YKwraTXzGSZMnOHa8F6+TYC7xN2iEBzEXUuuB5N95rK0/O2s+yYAdpxWI+J9diURo8yjfkDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"58aa95bd4aea2e3d5ce172f554cc5b5d963a0d882a359752ad3c4d5fbcbff939","last_reissued_at":"2026-07-05T06:14:21.143068Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:14:21.143068Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Benchmarking state-of-the-art gradient boosting algorithms for classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Adam Zagda\\'nski, Piotr Florek","submitted_at":"2023-05-26T17:06:15Z","abstract_excerpt":"This work explores the use of gradient boosting in the context of classification. Four popular implementations, including original GBM algorithm and selected state-of-the-art gradient boosting frameworks (i.e. XGBoost, LightGBM and CatBoost), have been thoroughly compared on several publicly available real-world datasets of sufficient diversity. In the study, special emphasis was placed on hyperparameter optimization, specifically comparing two tuning strategies, i.e. randomized search and Bayesian optimization using the Tree-stuctured Parzen Estimator. The performance of considered methods wa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.17094","kind":"arxiv","version":1},"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/2305.17094/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2305.17094","created_at":"2026-07-05T06:14:21.143129+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.17094v1","created_at":"2026-07-05T06:14:21.143129+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.17094","created_at":"2026-07-05T06:14:21.143129+00:00"},{"alias_kind":"pith_short_12","alias_value":"LCVJLPKK5IXD","created_at":"2026-07-05T06:14:21.143129+00:00"},{"alias_kind":"pith_short_16","alias_value":"LCVJLPKK5IXD2XHB","created_at":"2026-07-05T06:14:21.143129+00:00"},{"alias_kind":"pith_short_8","alias_value":"LCVJLPKK","created_at":"2026-07-05T06:14:21.143129+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.06433","citing_title":"eegFloss: A Python package for refining sleep EEG recordings using machine learning models","ref_index":88,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LCVJLPKK5IXD2XHBOL2VJTC3LW","json":"https://pith.science/pith/LCVJLPKK5IXD2XHBOL2VJTC3LW.json","graph_json":"https://pith.science/api/pith-number/LCVJLPKK5IXD2XHBOL2VJTC3LW/graph.json","events_json":"https://pith.science/api/pith-number/LCVJLPKK5IXD2XHBOL2VJTC3LW/events.json","paper":"https://pith.science/paper/LCVJLPKK"},"agent_actions":{"view_html":"https://pith.science/pith/LCVJLPKK5IXD2XHBOL2VJTC3LW","download_json":"https://pith.science/pith/LCVJLPKK5IXD2XHBOL2VJTC3LW.json","view_paper":"https://pith.science/paper/LCVJLPKK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.17094&json=true","fetch_graph":"https://pith.science/api/pith-number/LCVJLPKK5IXD2XHBOL2VJTC3LW/graph.json","fetch_events":"https://pith.science/api/pith-number/LCVJLPKK5IXD2XHBOL2VJTC3LW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LCVJLPKK5IXD2XHBOL2VJTC3LW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LCVJLPKK5IXD2XHBOL2VJTC3LW/action/storage_attestation","attest_author":"https://pith.science/pith/LCVJLPKK5IXD2XHBOL2VJTC3LW/action/author_attestation","sign_citation":"https://pith.science/pith/LCVJLPKK5IXD2XHBOL2VJTC3LW/action/citation_signature","submit_replication":"https://pith.science/pith/LCVJLPKK5IXD2XHBOL2VJTC3LW/action/replication_record"}},"created_at":"2026-07-05T06:14:21.143129+00:00","updated_at":"2026-07-05T06:14:21.143129+00:00"}