{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:BIIRNOPILGXCJHXN47XLPR5XGT","short_pith_number":"pith:BIIRNOPI","schema_version":"1.0","canonical_sha256":"0a1116b9e859ae249eede7eeb7c7b734cd89fdd5242b94985bc11142e3ff8b69","source":{"kind":"arxiv","id":"2008.05756","version":1},"attestation_state":"computed","paper":{"title":"Metrics for Multi-Class Classification: an Overview","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Enrico Bagli, Giorgio Visani, Margherita Grandini","submitted_at":"2020-08-13T08:41:44Z","abstract_excerpt":"Classification tasks in machine learning involving more than two classes are known by the name of \"multi-class classification\". Performance indicators are very useful when the aim is to evaluate and compare different classification models or machine learning techniques. Many metrics come in handy to test the ability of a multi-class classifier. Those metrics turn out to be useful at different stage of the development process, e.g. comparing the performance of two different models or analysing the behaviour of the same model by tuning different parameters. In this white paper we review a list o"},"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":"2008.05756","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-08-13T08:41:44Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"fb43cc9cdfc3404df177b9db2bab28b8cc94ee7fa45f06020f00d469a02e7ba5","abstract_canon_sha256":"bfb6872f51017d041878263893490f49578a491bc357599e7ad93fd3572d19e9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:26:56.949032Z","signature_b64":"1U3imhEkdgXCY6gptu3dg2UXQ0nQLN6Y9lmj37OqTi2G6vOpb2KFyQlyW5r76QfSvWTolPguR6xt3XspUhC6BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a1116b9e859ae249eede7eeb7c7b734cd89fdd5242b94985bc11142e3ff8b69","last_reissued_at":"2026-07-05T01:26:56.948581Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:26:56.948581Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Metrics for Multi-Class Classification: an Overview","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Enrico Bagli, Giorgio Visani, Margherita Grandini","submitted_at":"2020-08-13T08:41:44Z","abstract_excerpt":"Classification tasks in machine learning involving more than two classes are known by the name of \"multi-class classification\". Performance indicators are very useful when the aim is to evaluate and compare different classification models or machine learning techniques. Many metrics come in handy to test the ability of a multi-class classifier. Those metrics turn out to be useful at different stage of the development process, e.g. comparing the performance of two different models or analysing the behaviour of the same model by tuning different parameters. In this white paper we review a list o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.05756","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2008.05756/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":"2008.05756","created_at":"2026-07-05T01:26:56.948670+00:00"},{"alias_kind":"arxiv_version","alias_value":"2008.05756v1","created_at":"2026-07-05T01:26:56.948670+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.05756","created_at":"2026-07-05T01:26:56.948670+00:00"},{"alias_kind":"pith_short_12","alias_value":"BIIRNOPILGXC","created_at":"2026-07-05T01:26:56.948670+00:00"},{"alias_kind":"pith_short_16","alias_value":"BIIRNOPILGXCJHXN","created_at":"2026-07-05T01:26:56.948670+00:00"},{"alias_kind":"pith_short_8","alias_value":"BIIRNOPI","created_at":"2026-07-05T01:26:56.948670+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.01129","citing_title":"Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set","ref_index":50,"is_internal_anchor":false},{"citing_arxiv_id":"2605.29170","citing_title":"UA-Legal-Bench: A Benchmark for Evaluating Large Language Models on Ukrainian Legal Reasoning","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2501.16150","citing_title":"A Comprehensive Survey of Agents for Computer Use: Foundations, Challenges, and Future Directions","ref_index":45,"is_internal_anchor":false},{"citing_arxiv_id":"2605.20536","citing_title":"HADS-Net:A Hybrid Attention-Augmented Dual-Stream Network with Physics-Informed Augmentation for Breast Ultrasound Image Classification","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09565","citing_title":"Online Set Learning from Precision and Recall Feedback","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06020","citing_title":"Solving Constrained Affine Heaviside Composite Optimization Problems by a Progressive IP Approach","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01129","citing_title":"Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set","ref_index":50,"is_internal_anchor":false},{"citing_arxiv_id":"2605.03624","citing_title":"Annotation Quality in Aspect-Based Sentiment Analysis: A Case Study Comparing Experts, Students, Crowdworkers, and Large Language Model","ref_index":28,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BIIRNOPILGXCJHXN47XLPR5XGT","json":"https://pith.science/pith/BIIRNOPILGXCJHXN47XLPR5XGT.json","graph_json":"https://pith.science/api/pith-number/BIIRNOPILGXCJHXN47XLPR5XGT/graph.json","events_json":"https://pith.science/api/pith-number/BIIRNOPILGXCJHXN47XLPR5XGT/events.json","paper":"https://pith.science/paper/BIIRNOPI"},"agent_actions":{"view_html":"https://pith.science/pith/BIIRNOPILGXCJHXN47XLPR5XGT","download_json":"https://pith.science/pith/BIIRNOPILGXCJHXN47XLPR5XGT.json","view_paper":"https://pith.science/paper/BIIRNOPI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2008.05756&json=true","fetch_graph":"https://pith.science/api/pith-number/BIIRNOPILGXCJHXN47XLPR5XGT/graph.json","fetch_events":"https://pith.science/api/pith-number/BIIRNOPILGXCJHXN47XLPR5XGT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BIIRNOPILGXCJHXN47XLPR5XGT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BIIRNOPILGXCJHXN47XLPR5XGT/action/storage_attestation","attest_author":"https://pith.science/pith/BIIRNOPILGXCJHXN47XLPR5XGT/action/author_attestation","sign_citation":"https://pith.science/pith/BIIRNOPILGXCJHXN47XLPR5XGT/action/citation_signature","submit_replication":"https://pith.science/pith/BIIRNOPILGXCJHXN47XLPR5XGT/action/replication_record"}},"created_at":"2026-07-05T01:26:56.948670+00:00","updated_at":"2026-07-05T01:26:56.948670+00:00"}