{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:JRI4H7LAZCZCCW7T5TV2U3KL6D","short_pith_number":"pith:JRI4H7LA","schema_version":"1.0","canonical_sha256":"4c51c3fd60c8b2215bf3ecebaa6d4bf0f3a4e66fe041ffc24640545b5c0d59d9","source":{"kind":"arxiv","id":"1905.03438","version":1},"attestation_state":"computed","paper":{"title":"Two-stage Best-scored Random Forest for Large-scale Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Hanyuan Hang, Johan A.K. Suykens, Yingyi Chen","submitted_at":"2019-05-09T04:20:48Z","abstract_excerpt":"We propose a novel method designed for large-scale regression problems, namely the two-stage best-scored random forest (TBRF). \"Best-scored\" means to select one regression tree with the best empirical performance out of a certain number of purely random regression tree candidates, and \"two-stage\" means to divide the original random tree splitting procedure into two: In stage one, the feature space is partitioned into non-overlapping cells; in stage two, child trees grow separately on these cells. The strengths of this algorithm can be summarized as follows: First of all, the pure randomness in"},"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":"1905.03438","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-05-09T04:20:48Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"f4d8a2e312516a92c12a877f9737276ba2af67137d6d8340fd4ad60a85c1a410","abstract_canon_sha256":"008da5329be9a189f3a9fb6ee706fefb9615f2b23cf7d3506580395808779c3d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:39.916642Z","signature_b64":"uedh0zNniP2iBqFXuZwzuzkx6qWGaGdRsAIwpAU36AfXn1se8SEQ8Zz0tkTCkjMlPs1BPaupX1SKu9dL8CbmBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4c51c3fd60c8b2215bf3ecebaa6d4bf0f3a4e66fe041ffc24640545b5c0d59d9","last_reissued_at":"2026-05-17T23:46:39.915946Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:39.915946Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Two-stage Best-scored Random Forest for Large-scale Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Hanyuan Hang, Johan A.K. Suykens, Yingyi Chen","submitted_at":"2019-05-09T04:20:48Z","abstract_excerpt":"We propose a novel method designed for large-scale regression problems, namely the two-stage best-scored random forest (TBRF). \"Best-scored\" means to select one regression tree with the best empirical performance out of a certain number of purely random regression tree candidates, and \"two-stage\" means to divide the original random tree splitting procedure into two: In stage one, the feature space is partitioned into non-overlapping cells; in stage two, child trees grow separately on these cells. The strengths of this algorithm can be summarized as follows: First of all, the pure randomness in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.03438","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":""},"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":"1905.03438","created_at":"2026-05-17T23:46:39.916054+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.03438v1","created_at":"2026-05-17T23:46:39.916054+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.03438","created_at":"2026-05-17T23:46:39.916054+00:00"},{"alias_kind":"pith_short_12","alias_value":"JRI4H7LAZCZC","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_16","alias_value":"JRI4H7LAZCZCCW7T","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_8","alias_value":"JRI4H7LA","created_at":"2026-05-18T12:33:21.387695+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JRI4H7LAZCZCCW7T5TV2U3KL6D","json":"https://pith.science/pith/JRI4H7LAZCZCCW7T5TV2U3KL6D.json","graph_json":"https://pith.science/api/pith-number/JRI4H7LAZCZCCW7T5TV2U3KL6D/graph.json","events_json":"https://pith.science/api/pith-number/JRI4H7LAZCZCCW7T5TV2U3KL6D/events.json","paper":"https://pith.science/paper/JRI4H7LA"},"agent_actions":{"view_html":"https://pith.science/pith/JRI4H7LAZCZCCW7T5TV2U3KL6D","download_json":"https://pith.science/pith/JRI4H7LAZCZCCW7T5TV2U3KL6D.json","view_paper":"https://pith.science/paper/JRI4H7LA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.03438&json=true","fetch_graph":"https://pith.science/api/pith-number/JRI4H7LAZCZCCW7T5TV2U3KL6D/graph.json","fetch_events":"https://pith.science/api/pith-number/JRI4H7LAZCZCCW7T5TV2U3KL6D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JRI4H7LAZCZCCW7T5TV2U3KL6D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JRI4H7LAZCZCCW7T5TV2U3KL6D/action/storage_attestation","attest_author":"https://pith.science/pith/JRI4H7LAZCZCCW7T5TV2U3KL6D/action/author_attestation","sign_citation":"https://pith.science/pith/JRI4H7LAZCZCCW7T5TV2U3KL6D/action/citation_signature","submit_replication":"https://pith.science/pith/JRI4H7LAZCZCCW7T5TV2U3KL6D/action/replication_record"}},"created_at":"2026-05-17T23:46:39.916054+00:00","updated_at":"2026-05-17T23:46:39.916054+00:00"}