{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:MNRKR4NLBDH2C7C7SO5J2KBIPP","short_pith_number":"pith:MNRKR4NL","schema_version":"1.0","canonical_sha256":"6362a8f1ab08cfa17c5f93ba9d28287bd6b7f6402377d2813f6eaef399803026","source":{"kind":"arxiv","id":"1708.07180","version":2},"attestation_state":"computed","paper":{"title":"Bootstrapping the Out-of-sample Predictions for Efficient and Accurate Cross-Validation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Elissavet Greasidou, Giorgos Borboudakis, Ioannis Tsamardinos, Michalis Tsagris","submitted_at":"2017-08-23T20:30:07Z","abstract_excerpt":"Cross-Validation (CV), and out-of-sample performance-estimation protocols in general, are often employed both for (a) selecting the optimal combination of algorithms and values of hyper-parameters (called a configuration) for producing the final predictive model, and (b) estimating the predictive performance of the final model. However, the cross-validated performance of the best configuration is optimistically biased. We present an efficient bootstrap method that corrects for the bias, called Bootstrap Bias Corrected CV (BBC-CV). BBC-CV's main idea is to bootstrap the whole process of selecti"},"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":"1708.07180","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-08-23T20:30:07Z","cross_cats_sorted":[],"title_canon_sha256":"cdd75d0e226538bd10cf83afe307355a1db990792ad494c4459fc12e1e665dda","abstract_canon_sha256":"d6162d42f8224a5e2181565cbd8996f0ebe8c578ba117eb3195bf59b9515523f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:36:42.845616Z","signature_b64":"WiqgHIcQXcgXhrS7S4A41/toIAXGOcjmtS0uYcIsT2wIxa0R/a6yWtr6N9RMEw0J797iBch4lLfozriDmHPVDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6362a8f1ab08cfa17c5f93ba9d28287bd6b7f6402377d2813f6eaef399803026","last_reissued_at":"2026-05-18T00:36:42.844919Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:36:42.844919Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bootstrapping the Out-of-sample Predictions for Efficient and Accurate Cross-Validation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Elissavet Greasidou, Giorgos Borboudakis, Ioannis Tsamardinos, Michalis Tsagris","submitted_at":"2017-08-23T20:30:07Z","abstract_excerpt":"Cross-Validation (CV), and out-of-sample performance-estimation protocols in general, are often employed both for (a) selecting the optimal combination of algorithms and values of hyper-parameters (called a configuration) for producing the final predictive model, and (b) estimating the predictive performance of the final model. However, the cross-validated performance of the best configuration is optimistically biased. We present an efficient bootstrap method that corrects for the bias, called Bootstrap Bias Corrected CV (BBC-CV). BBC-CV's main idea is to bootstrap the whole process of selecti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.07180","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":"1708.07180","created_at":"2026-05-18T00:36:42.845029+00:00"},{"alias_kind":"arxiv_version","alias_value":"1708.07180v2","created_at":"2026-05-18T00:36:42.845029+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.07180","created_at":"2026-05-18T00:36:42.845029+00:00"},{"alias_kind":"pith_short_12","alias_value":"MNRKR4NLBDH2","created_at":"2026-05-18T12:31:31.346846+00:00"},{"alias_kind":"pith_short_16","alias_value":"MNRKR4NLBDH2C7C7","created_at":"2026-05-18T12:31:31.346846+00:00"},{"alias_kind":"pith_short_8","alias_value":"MNRKR4NL","created_at":"2026-05-18T12:31:31.346846+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/MNRKR4NLBDH2C7C7SO5J2KBIPP","json":"https://pith.science/pith/MNRKR4NLBDH2C7C7SO5J2KBIPP.json","graph_json":"https://pith.science/api/pith-number/MNRKR4NLBDH2C7C7SO5J2KBIPP/graph.json","events_json":"https://pith.science/api/pith-number/MNRKR4NLBDH2C7C7SO5J2KBIPP/events.json","paper":"https://pith.science/paper/MNRKR4NL"},"agent_actions":{"view_html":"https://pith.science/pith/MNRKR4NLBDH2C7C7SO5J2KBIPP","download_json":"https://pith.science/pith/MNRKR4NLBDH2C7C7SO5J2KBIPP.json","view_paper":"https://pith.science/paper/MNRKR4NL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1708.07180&json=true","fetch_graph":"https://pith.science/api/pith-number/MNRKR4NLBDH2C7C7SO5J2KBIPP/graph.json","fetch_events":"https://pith.science/api/pith-number/MNRKR4NLBDH2C7C7SO5J2KBIPP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MNRKR4NLBDH2C7C7SO5J2KBIPP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MNRKR4NLBDH2C7C7SO5J2KBIPP/action/storage_attestation","attest_author":"https://pith.science/pith/MNRKR4NLBDH2C7C7SO5J2KBIPP/action/author_attestation","sign_citation":"https://pith.science/pith/MNRKR4NLBDH2C7C7SO5J2KBIPP/action/citation_signature","submit_replication":"https://pith.science/pith/MNRKR4NLBDH2C7C7SO5J2KBIPP/action/replication_record"}},"created_at":"2026-05-18T00:36:42.845029+00:00","updated_at":"2026-05-18T00:36:42.845029+00:00"}