{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:DLDP7TH6FODIHWLPEV7ALJB5VL","short_pith_number":"pith:DLDP7TH6","schema_version":"1.0","canonical_sha256":"1ac6ffccfe2b8683d96f257e05a43daad01278253300a9552e66e230af116bd5","source":{"kind":"arxiv","id":"1804.09331","version":2},"attestation_state":"computed","paper":{"title":"Where are we now? A large benchmark study of recent symbolic regression methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Jason H. Moore, Patryk Orzechowski, William La Cava","submitted_at":"2018-04-25T02:58:13Z","abstract_excerpt":"In this paper we provide a broad benchmarking of recent genetic programming approaches to symbolic regression in the context of state of the art machine learning approaches. We use a set of nearly 100 regression benchmark problems culled from open source repositories across the web. We conduct a rigorous benchmarking of four recent symbolic regression approaches as well as nine machine learning approaches from scikit-learn. The results suggest that symbolic regression performs strongly compared to state-of-the-art gradient boosting algorithms, although in terms of running times is among the sl"},"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":"1804.09331","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-04-25T02:58:13Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"85aab70bd4e2462ec63a41a4077c9f26c4b1c060c2174fc5e7c47d095c80402b","abstract_canon_sha256":"cca98e84dad32b821e9b4a01293a88a7ef2407c925ace50a4082ce0b6b7d69fd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:13:59.918501Z","signature_b64":"6llCMo0Z8AYPNf/CXMv0Is5HEPWJ6NN1BOv1l5j1DTeXLeDhFETKsZd9xiSmbT8Y1J6KCuTnv+0IhsHqAJSSBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1ac6ffccfe2b8683d96f257e05a43daad01278253300a9552e66e230af116bd5","last_reissued_at":"2026-05-18T00:13:59.917873Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:13:59.917873Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Where are we now? A large benchmark study of recent symbolic regression methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Jason H. Moore, Patryk Orzechowski, William La Cava","submitted_at":"2018-04-25T02:58:13Z","abstract_excerpt":"In this paper we provide a broad benchmarking of recent genetic programming approaches to symbolic regression in the context of state of the art machine learning approaches. We use a set of nearly 100 regression benchmark problems culled from open source repositories across the web. We conduct a rigorous benchmarking of four recent symbolic regression approaches as well as nine machine learning approaches from scikit-learn. The results suggest that symbolic regression performs strongly compared to state-of-the-art gradient boosting algorithms, although in terms of running times is among the sl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.09331","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":""},"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":"1804.09331","created_at":"2026-05-18T00:13:59.917958+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.09331v2","created_at":"2026-05-18T00:13:59.917958+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.09331","created_at":"2026-05-18T00:13:59.917958+00:00"},{"alias_kind":"pith_short_12","alias_value":"DLDP7TH6FODI","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_16","alias_value":"DLDP7TH6FODIHWLP","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_8","alias_value":"DLDP7TH6","created_at":"2026-05-18T12:32:19.392346+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/DLDP7TH6FODIHWLPEV7ALJB5VL","json":"https://pith.science/pith/DLDP7TH6FODIHWLPEV7ALJB5VL.json","graph_json":"https://pith.science/api/pith-number/DLDP7TH6FODIHWLPEV7ALJB5VL/graph.json","events_json":"https://pith.science/api/pith-number/DLDP7TH6FODIHWLPEV7ALJB5VL/events.json","paper":"https://pith.science/paper/DLDP7TH6"},"agent_actions":{"view_html":"https://pith.science/pith/DLDP7TH6FODIHWLPEV7ALJB5VL","download_json":"https://pith.science/pith/DLDP7TH6FODIHWLPEV7ALJB5VL.json","view_paper":"https://pith.science/paper/DLDP7TH6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.09331&json=true","fetch_graph":"https://pith.science/api/pith-number/DLDP7TH6FODIHWLPEV7ALJB5VL/graph.json","fetch_events":"https://pith.science/api/pith-number/DLDP7TH6FODIHWLPEV7ALJB5VL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DLDP7TH6FODIHWLPEV7ALJB5VL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DLDP7TH6FODIHWLPEV7ALJB5VL/action/storage_attestation","attest_author":"https://pith.science/pith/DLDP7TH6FODIHWLPEV7ALJB5VL/action/author_attestation","sign_citation":"https://pith.science/pith/DLDP7TH6FODIHWLPEV7ALJB5VL/action/citation_signature","submit_replication":"https://pith.science/pith/DLDP7TH6FODIHWLPEV7ALJB5VL/action/replication_record"}},"created_at":"2026-05-18T00:13:59.917958+00:00","updated_at":"2026-05-18T00:13:59.917958+00:00"}