{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-11250340","patent":{"patent_number":"US-11250340","title":"Feature contributors and influencers in machine learned predictive models","assignee":null,"inventors":[],"filing_date":"2017-12-14T00:00:00.000Z","publication_date":"2022-02-15T00:00:00.000Z","cpc_codes":["G06Q","G06F","G06F","G06F","G06N","G06N","G06N","G06N","G06F","G06N","G06N","G06Q"],"num_claims":20,"abstract":"In an example, for each feature of one or more features of a target sample data, feature values for one or more pseudo-samples are generated using, localized stratified sampling. The one or more pseudo-samples are fed into the trained machine learned model to obtain their prediction values. A piecewise linear regression model is trained using the one or more pseudo-samples and their prediction values, the piecewise linear regression model having two coefficients for each feature, a first coefficient describing prediction change when a corresponding feature value is increased and a second coefficient describing prediction change when a corresponding feature value is decreased. A top positive feature influencer is identified based on a feature of the one or more features of the target sample having a greatest magnitude of positive first coefficient or greatest magnitude of negative second coefficient. A top negative feature influencer is identified based on a feature of the one or more features of the target sample having a greatest magnitude of negative first coefficient or greatest magnitude of positive second coefficient. A top feature contributor is identified based on a feature of the one or more features of the target sample having a greatest magnitude of a combination of second coefficient and feature value in the target sample data."},"analysis":{"summary":null,"layman_explanation":null,"technical_analysis":null,"business_analysis":null,"faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Feature contributors and influencers in machine learned predictive models","description":"In an example, for each feature of one or more features of a target sample data, feature values for one or more pseudo-samples are generated using, localized stratified sampling. The one or more pseud","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-11250340","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-11250340","citation_suggestion":"Patentable. \"Feature contributors and influencers in machine learned predictive models\" (US-11250340). https://patentable.app/patents/US-11250340","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-11250340","json":"https://patentable.app/api/llm-context/US-11250340","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-30T23:57:10.404Z"}