{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:MZV6NO47OJ4AI77XMYDVIXC6JB","short_pith_number":"pith:MZV6NO47","schema_version":"1.0","canonical_sha256":"666be6bb9f7278047ff76607545c5e4878325b2f8f50ecd5d7101f644825593e","source":{"kind":"arxiv","id":"2105.11724","version":3},"attestation_state":"computed","paper":{"title":"SHAFF: Fast and consistent SHApley eFfect estimates via random Forests","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Cl\\'ement B\\'enard (LPSM (UMR\\_8001)), Erwan Scornet (CMAP), G\\'erard Biau (LPSM (UMR\\_8001)), S\\'ebastien Da Veiga","submitted_at":"2021-05-25T07:48:07Z","abstract_excerpt":"Interpretability of learning algorithms is crucial for applications involving critical decisions, and variable importance is one of the main interpretation tools. Shapley effects are now widely used to interpret both tree ensembles and neural networks, as they can efficiently handle dependence and interactions in the data, as opposed to most other variable importance measures. However, estimating Shapley effects is a challenging task, because of the computational complexity and the conditional expectation estimates. Accordingly, existing Shapley algorithms have flaws: a costly running time, or"},"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":"2105.11724","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-05-25T07:48:07Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"aaf13e8dd2cda48dacb37fa969b4d87e18cf7d59db69d51d1277a7e4389cb76f","abstract_canon_sha256":"a3f2465a3d7b53a337798ebafabedd39a3b4bbead656445b571b79ad20727494"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:53:36.231303Z","signature_b64":"MGGvTVlEkc2S4nx5kHGHKWwslmrU6cf7/u76Q67d7XPxhlEMX8464kTz0boNaJ11Pc+LAzV0XfJ8WccUj4n+AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"666be6bb9f7278047ff76607545c5e4878325b2f8f50ecd5d7101f644825593e","last_reissued_at":"2026-07-05T03:53:36.230763Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:53:36.230763Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SHAFF: Fast and consistent SHApley eFfect estimates via random Forests","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Cl\\'ement B\\'enard (LPSM (UMR\\_8001)), Erwan Scornet (CMAP), G\\'erard Biau (LPSM (UMR\\_8001)), S\\'ebastien Da Veiga","submitted_at":"2021-05-25T07:48:07Z","abstract_excerpt":"Interpretability of learning algorithms is crucial for applications involving critical decisions, and variable importance is one of the main interpretation tools. Shapley effects are now widely used to interpret both tree ensembles and neural networks, as they can efficiently handle dependence and interactions in the data, as opposed to most other variable importance measures. However, estimating Shapley effects is a challenging task, because of the computational complexity and the conditional expectation estimates. Accordingly, existing Shapley algorithms have flaws: a costly running time, or"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.11724","kind":"arxiv","version":3},"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/2105.11724/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":"2105.11724","created_at":"2026-07-05T03:53:36.230837+00:00"},{"alias_kind":"arxiv_version","alias_value":"2105.11724v3","created_at":"2026-07-05T03:53:36.230837+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.11724","created_at":"2026-07-05T03:53:36.230837+00:00"},{"alias_kind":"pith_short_12","alias_value":"MZV6NO47OJ4A","created_at":"2026-07-05T03:53:36.230837+00:00"},{"alias_kind":"pith_short_16","alias_value":"MZV6NO47OJ4AI77X","created_at":"2026-07-05T03:53:36.230837+00:00"},{"alias_kind":"pith_short_8","alias_value":"MZV6NO47","created_at":"2026-07-05T03:53:36.230837+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/MZV6NO47OJ4AI77XMYDVIXC6JB","json":"https://pith.science/pith/MZV6NO47OJ4AI77XMYDVIXC6JB.json","graph_json":"https://pith.science/api/pith-number/MZV6NO47OJ4AI77XMYDVIXC6JB/graph.json","events_json":"https://pith.science/api/pith-number/MZV6NO47OJ4AI77XMYDVIXC6JB/events.json","paper":"https://pith.science/paper/MZV6NO47"},"agent_actions":{"view_html":"https://pith.science/pith/MZV6NO47OJ4AI77XMYDVIXC6JB","download_json":"https://pith.science/pith/MZV6NO47OJ4AI77XMYDVIXC6JB.json","view_paper":"https://pith.science/paper/MZV6NO47","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2105.11724&json=true","fetch_graph":"https://pith.science/api/pith-number/MZV6NO47OJ4AI77XMYDVIXC6JB/graph.json","fetch_events":"https://pith.science/api/pith-number/MZV6NO47OJ4AI77XMYDVIXC6JB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MZV6NO47OJ4AI77XMYDVIXC6JB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MZV6NO47OJ4AI77XMYDVIXC6JB/action/storage_attestation","attest_author":"https://pith.science/pith/MZV6NO47OJ4AI77XMYDVIXC6JB/action/author_attestation","sign_citation":"https://pith.science/pith/MZV6NO47OJ4AI77XMYDVIXC6JB/action/citation_signature","submit_replication":"https://pith.science/pith/MZV6NO47OJ4AI77XMYDVIXC6JB/action/replication_record"}},"created_at":"2026-07-05T03:53:36.230837+00:00","updated_at":"2026-07-05T03:53:36.230837+00:00"}