{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:4NBGQY266J5LWTON2MF5LXIOVQ","short_pith_number":"pith:4NBGQY26","schema_version":"1.0","canonical_sha256":"e34268635ef27abb4dcdd30bd5dd0eac3ced31a91e49ba2589c471da6e6c1aad","source":{"kind":"arxiv","id":"1709.07150","version":1},"attestation_state":"computed","paper":{"title":"Feature Engineering for Predictive Modeling using Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.AI","authors_text":"Deepak Turaga, Horst Samulowitz, Udayan Khurana","submitted_at":"2017-09-21T04:04:43Z","abstract_excerpt":"Feature engineering is a crucial step in the process of predictive modeling. It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target. However, there is no well-defined basis for performing effective feature engineering. It involves domain knowledge, intuition, and most of all, a lengthy process of trial and error. The human attention involved in overseeing this process significantly influences the cost of model generation. We present a new framework to automate feature engineering. It is"},"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":"1709.07150","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-09-21T04:04:43Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"0de489d9998d59c340f3d1a1a385acf4e0d09e7725164ab14a52dc6fdf2b7ac1","abstract_canon_sha256":"2500bc3a280119f28b6490948b51eb600cd84ccc138e3dd787a5c4a2faa7ab2a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:34:36.450775Z","signature_b64":"4HFLE8dRGgprE2A7cjzrrZR8KFufhyv6ETWdEioVUDBsiHxBboYjJJyVcufJu40ZFSdoSreEfUB3XMUV5Q+mBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e34268635ef27abb4dcdd30bd5dd0eac3ced31a91e49ba2589c471da6e6c1aad","last_reissued_at":"2026-05-18T00:34:36.450274Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:34:36.450274Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Feature Engineering for Predictive Modeling using Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.AI","authors_text":"Deepak Turaga, Horst Samulowitz, Udayan Khurana","submitted_at":"2017-09-21T04:04:43Z","abstract_excerpt":"Feature engineering is a crucial step in the process of predictive modeling. It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target. However, there is no well-defined basis for performing effective feature engineering. It involves domain knowledge, intuition, and most of all, a lengthy process of trial and error. The human attention involved in overseeing this process significantly influences the cost of model generation. We present a new framework to automate feature engineering. It is"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.07150","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":"1709.07150","created_at":"2026-05-18T00:34:36.450368+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.07150v1","created_at":"2026-05-18T00:34:36.450368+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.07150","created_at":"2026-05-18T00:34:36.450368+00:00"},{"alias_kind":"pith_short_12","alias_value":"4NBGQY266J5L","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_16","alias_value":"4NBGQY266J5LWTON","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_8","alias_value":"4NBGQY26","created_at":"2026-05-18T12:31:00.734936+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1906.12348","citing_title":"MLFriend: Interactive Prediction Task Recommendation for Event-Driven Time-Series Data","ref_index":13,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4NBGQY266J5LWTON2MF5LXIOVQ","json":"https://pith.science/pith/4NBGQY266J5LWTON2MF5LXIOVQ.json","graph_json":"https://pith.science/api/pith-number/4NBGQY266J5LWTON2MF5LXIOVQ/graph.json","events_json":"https://pith.science/api/pith-number/4NBGQY266J5LWTON2MF5LXIOVQ/events.json","paper":"https://pith.science/paper/4NBGQY26"},"agent_actions":{"view_html":"https://pith.science/pith/4NBGQY266J5LWTON2MF5LXIOVQ","download_json":"https://pith.science/pith/4NBGQY266J5LWTON2MF5LXIOVQ.json","view_paper":"https://pith.science/paper/4NBGQY26","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.07150&json=true","fetch_graph":"https://pith.science/api/pith-number/4NBGQY266J5LWTON2MF5LXIOVQ/graph.json","fetch_events":"https://pith.science/api/pith-number/4NBGQY266J5LWTON2MF5LXIOVQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4NBGQY266J5LWTON2MF5LXIOVQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4NBGQY266J5LWTON2MF5LXIOVQ/action/storage_attestation","attest_author":"https://pith.science/pith/4NBGQY266J5LWTON2MF5LXIOVQ/action/author_attestation","sign_citation":"https://pith.science/pith/4NBGQY266J5LWTON2MF5LXIOVQ/action/citation_signature","submit_replication":"https://pith.science/pith/4NBGQY266J5LWTON2MF5LXIOVQ/action/replication_record"}},"created_at":"2026-05-18T00:34:36.450368+00:00","updated_at":"2026-05-18T00:34:36.450368+00:00"}