Data leakage is a well-known problem in machine learning. Data leakage occurs when information from outside the training dataset is used to create a model. This phenomenon renders a model excessively optimistic or even useless in the real world, since the model tends to leverage greatly on the unfairly acquired information. To date, detection of data leakages occurs most-mortem using runtime methods. In this paper, we develop a static data leakage analysis to detect several instances of data leakages during development time. Our analysis is constructed to be light weight so that it can be performed in seconds. We have integrated our analysis into the NBLyzer static analyzer. To the best of our knowledge, we propose the first static detection of data leakages.<\/p>\n","protected":false},"excerpt":{"rendered":"
Data leakage is a well-known problem in machine learning. Data leakage occurs when information from outside the training dataset is used to create a model. This phenomenon renders a model excessively optimistic or even useless in the real world, since the model tends to leverage greatly on the unfairly acquired information. To date, detection of […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13563,13560],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[248116,248224,252028],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-849874","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-data-platform-analytics","msr-research-area-programming-languages-software-engineering","msr-locale-en_us","msr-field-of-study-data-science","msr-field-of-study-software-deployment","msr-field-of-study-static-program-analysis"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2022-6-14","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/06\/SOAP.pdf","id":"849877","title":"soap","label_id":"243132","label":0}],"msr_related_uploader":"","msr_attachments":[{"id":849877,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/06\/SOAP.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"Pavle Suboti\u0107","user_id":40420,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Pavle Suboti\u0107"},{"type":"text","value":"Uros Bojani\u0107","user_id":0,"rest_url":false},{"type":"text","value":"Milan Stoji\u0107","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[851368],"msr_group":[],"msr_project":[849883],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":849883,"post_title":"NBLyzer: A static analysis framework for data science notebooks","post_name":"nblyzer-a-static-analysis-framework-for-data-science-notebooks","post_type":"msr-project","post_date":"2022-06-03 02:02:15","post_modified":"2022-06-03 07:04:04","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/nblyzer-a-static-analysis-framework-for-data-science-notebooks\/","post_excerpt":"A static analysis framework for data science notebooks Notebooks provide an interactive environment for programmers to develop code, analyse data and inject interleaved visualisations in a single environment. Despite their flexibility, a major pitfall that data scientists encounter is unexpected behaviour caused by the unique out-of-order execution model of notebooks. As a result, data scientists face various challenges ranging from notebook correctness, reproducibility and cleaning. 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