{"id":1063,"date":"2024-12-06T11:47:02","date_gmt":"2024-12-06T02:47:02","guid":{"rendered":"https:\/\/www.aicritique.org\/us\/?post_type=explainable&#038;p=1063"},"modified":"2024-12-06T14:44:19","modified_gmt":"2024-12-06T05:44:19","slug":"microsoft","status":"publish","type":"explainable","link":"https:\/\/www.aicritique.org\/us\/explainable\/microsoft\/","title":{"rendered":"Microsoft"},"content":{"rendered":"\n<ul class=\"wp-block-list\">\n<li><strong>InterpretML<\/strong>: An open-source library for understanding and interpreting machine learning models using methods like SHAP and LIME.<\/li>\n\n\n\n<li><strong><a href=\"https:\/\/www.aicritique.org\/us\/wp-admin\/post.php?post=1082&amp;action=edit\">Explainable Boosting Machine (EBM)<\/a><\/strong>: A glass-box model that allows data analysts to clearly understand the impact of each feature on model predictions.<\/li>\n\n\n\n<li><strong>Azure Machine Learning Interpretability Toolkit<\/strong>: Offers a suite of tools for investigating model predictions, enabling transparency in machine learning workflows.<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n","protected":false},"featured_media":0,"template":"","class_list":["post-1063","explainable","type-explainable","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.aicritique.org\/us\/wp-json\/wp\/v2\/explainable\/1063","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.aicritique.org\/us\/wp-json\/wp\/v2\/explainable"}],"about":[{"href":"https:\/\/www.aicritique.org\/us\/wp-json\/wp\/v2\/types\/explainable"}],"wp:attachment":[{"href":"https:\/\/www.aicritique.org\/us\/wp-json\/wp\/v2\/media?parent=1063"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}