Understanding prediction errors and determining how to fix them is critical to building effective predictive systems. In this paper, we delineate four types of prediction errors (mislabeling, representation, learner and boundary errors) and demonstrate that these four types characterize all prediction errors. In addition, we describe potential remedies and tools that can be used to reduce the uncertainty when trying to determine the source of a prediction error and when trying to take action to remove a prediction error.<\/p>\n","protected":false},"excerpt":{"rendered":"
Understanding prediction errors and determining how to fix them is critical to building effective predictive systems. In this paper, we delineate four types of prediction errors (mislabeling, representation, learner and boundary errors) and demonstrate that these four types characterize all prediction errors. In addition, we describe potential remedies and tools that can be used to […]<\/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":[13556],"msr-publication-type":[193718],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-323960","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2016-11-17","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-TR-2016-1105","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":"323963","msr_publicationurl":"https:\/\/arxiv.org\/abs\/1611.05955","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"predictionerrorstechreport","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/11\/PredictionErrorsTechReport.pdf","id":323963,"label_id":0},{"type":"url","title":"https:\/\/arxiv.org\/abs\/1611.05955","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":0,"url":"https:\/\/arxiv.org\/abs\/1611.05955"}],"msr-author-ordering":[{"type":"user_nicename","value":"meek","user_id":32868,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=meek"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[144941],"msr_project":[171459],"publication":[],"video":[],"download":[],"msr_publication_type":"techreport","related_content":{"projects":[{"ID":171459,"post_title":"Platform for Interactive Concept Learning (PICL)","post_name":"platform-for-interactive-concept-learning-picl","post_type":"msr-project","post_date":"2015-04-28 11:12:37","post_modified":"2018-12-03 15:04:35","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/platform-for-interactive-concept-learning-picl\/","post_excerpt":"Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. 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