| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_53f347c8f604f15767e8e73fec62095a |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N5-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N5-025
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N5-01
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N5-046 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N20-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N5-04 |
| filingDate |
2019-03-20-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate |
2022-12-20-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_b73ed722ea45c8ae2e638f12e40447d1
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_745ecce8be8742f29dbec81d34fd8b40
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_d0f56c5ec89009c31e789c4949c037b2
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_df6a88704cfa430aa249675f0c5254d5
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_c6901bb81fe96dd5ecc4219e579a2abe
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_5d1c286413344581a10450df7bc4e077 |
| publicationDate |
2022-12-20-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-11531915-B2 |
| titleOfInvention |
Method for generating rulesets using tree-based models for black-box machine learning explainability |
| abstract |
Herein are techniques to generate candidate rulesets for machine learning (ML) explainability (MLX) for black-box ML models. In an embodiment, an ML model generates classifications that each associates a distinct example with a label. A decision tree that, based on the classifications, contains tree nodes is received or generated. Each node contains label(s), a condition that identifies a feature of examples, and a split value for the feature. When a node has child nodes, the feature and the split value that are identified by the condition of the node are set to maximize information gain of the child nodes. Candidate rules are generated by traversing the tree. Each rule is built from a combination of nodes in a tree traversal path. Each rule contains a condition of at least one node and is assigned to a rule level. Candidate rules are subsequently optimized into an optimal ruleset for actual use. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2022358540-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11727284-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2021182698-A1 |
| priorityDate |
2019-03-20-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |