{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:YB7KFKDOV3IU4TSAG5OJBDCJNN","short_pith_number":"pith:YB7KFKDO","schema_version":"1.0","canonical_sha256":"c07ea2a86eaed14e4e40375c908c496b6393fbd4d8cfe047b31df4bba300b6e6","source":{"kind":"arxiv","id":"1706.06691","version":1},"attestation_state":"computed","paper":{"title":"Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Andrew Haines, Fabrizio Silvestri, Gabriele Tolomei, Mounia Lalmas","submitted_at":"2017-06-20T22:32:02Z","abstract_excerpt":"Machine-learned models are often described as \"black boxes\". In many real-world applications however, models may have to sacrifice predictive power in favour of human-interpretability. When this is the case, feature engineering becomes a crucial task, which requires significant and time-consuming human effort. Whilst some features are inherently static, representing properties that cannot be influenced (e.g., the age of an individual), others capture characteristics that could be adjusted (e.g., the daily amount of carbohydrates taken). Nonetheless, once a model is learned from the data, each "},"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":"1706.06691","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-20T22:32:02Z","cross_cats_sorted":[],"title_canon_sha256":"12bf634a518b13c166863b757af2415c31c7f6b76e1d3031442bbb43a772b498","abstract_canon_sha256":"6fc3416cd43a853497018d0e49cf21be8ed60b51a591b37ccb8537571f5e0cfd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:41:57.148252Z","signature_b64":"fxb3SNRl6kACFBj5sMoABlSoNwnvsdxH+dLVKys6AhkaqY6vnRX5KmASqmf6yzYhr/Nu/OywRBeUnVlwE/TnDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c07ea2a86eaed14e4e40375c908c496b6393fbd4d8cfe047b31df4bba300b6e6","last_reissued_at":"2026-05-18T00:41:57.147648Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:41:57.147648Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Andrew Haines, Fabrizio Silvestri, Gabriele Tolomei, Mounia Lalmas","submitted_at":"2017-06-20T22:32:02Z","abstract_excerpt":"Machine-learned models are often described as \"black boxes\". In many real-world applications however, models may have to sacrifice predictive power in favour of human-interpretability. When this is the case, feature engineering becomes a crucial task, which requires significant and time-consuming human effort. Whilst some features are inherently static, representing properties that cannot be influenced (e.g., the age of an individual), others capture characteristics that could be adjusted (e.g., the daily amount of carbohydrates taken). Nonetheless, once a model is learned from the data, each "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.06691","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":"1706.06691","created_at":"2026-05-18T00:41:57.147747+00:00"},{"alias_kind":"arxiv_version","alias_value":"1706.06691v1","created_at":"2026-05-18T00:41:57.147747+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.06691","created_at":"2026-05-18T00:41:57.147747+00:00"},{"alias_kind":"pith_short_12","alias_value":"YB7KFKDOV3IU","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_16","alias_value":"YB7KFKDOV3IU4TSA","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_8","alias_value":"YB7KFKDO","created_at":"2026-05-18T12:31:56.362134+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/YB7KFKDOV3IU4TSAG5OJBDCJNN","json":"https://pith.science/pith/YB7KFKDOV3IU4TSAG5OJBDCJNN.json","graph_json":"https://pith.science/api/pith-number/YB7KFKDOV3IU4TSAG5OJBDCJNN/graph.json","events_json":"https://pith.science/api/pith-number/YB7KFKDOV3IU4TSAG5OJBDCJNN/events.json","paper":"https://pith.science/paper/YB7KFKDO"},"agent_actions":{"view_html":"https://pith.science/pith/YB7KFKDOV3IU4TSAG5OJBDCJNN","download_json":"https://pith.science/pith/YB7KFKDOV3IU4TSAG5OJBDCJNN.json","view_paper":"https://pith.science/paper/YB7KFKDO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1706.06691&json=true","fetch_graph":"https://pith.science/api/pith-number/YB7KFKDOV3IU4TSAG5OJBDCJNN/graph.json","fetch_events":"https://pith.science/api/pith-number/YB7KFKDOV3IU4TSAG5OJBDCJNN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YB7KFKDOV3IU4TSAG5OJBDCJNN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YB7KFKDOV3IU4TSAG5OJBDCJNN/action/storage_attestation","attest_author":"https://pith.science/pith/YB7KFKDOV3IU4TSAG5OJBDCJNN/action/author_attestation","sign_citation":"https://pith.science/pith/YB7KFKDOV3IU4TSAG5OJBDCJNN/action/citation_signature","submit_replication":"https://pith.science/pith/YB7KFKDOV3IU4TSAG5OJBDCJNN/action/replication_record"}},"created_at":"2026-05-18T00:41:57.147747+00:00","updated_at":"2026-05-18T00:41:57.147747+00:00"}