{"id":524,"date":"2023-01-14T11:09:28","date_gmt":"2023-01-14T11:09:28","guid":{"rendered":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/aws-certified-machine-learning-specialty-mls-c01-question125\/"},"modified":"2023-01-14T11:09:28","modified_gmt":"2023-01-14T11:09:28","slug":"aws-certified-machine-learning-specialty-mls-c01-question125","status":"publish","type":"post","link":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/aws-certified-machine-learning-specialty-mls-c01-question125\/","title":{"rendered":"AWS Certified Machine Learning &#8211; Specialty MLS-C01 &#8211; Question125"},"content":{"rendered":"<div class=\"question\">A financial company is trying to detect credit card fraud. The company observed that, on average, 2% of credit card transactions were fraudulent. A data scientist trained a classifier on a year&#039;s worth of credit card transactions data. The model needs to identify the fraudulent transactions (positives) from the regular ones (negatives). The company&#039;s goal is to accurately capture as many positives as possible.<br \/>\nWhich metrics should the data scientist use to optimize the model? (Choose two.)<br \/><strong><br \/>A.<\/strong> Specificity<br \/><strong>B.<\/strong> False positive rate<br \/><strong>C.<\/strong> Accuracy<br \/><strong>D.<\/strong> Area under the precision-recall curve<br \/><strong>E.<\/strong> True positive rate<\/div>\n<p><\/p>\n<style> .hidden-div{ display:none } <\/style>\n<p>\t\t\t\t\t\t\t<button onclick=\"getElementById('hidden-div').style.display = 'block'\"> Show Answer <\/button> <button onclick=\"getElementById('hidden-div').style.display = 'none'\">Hide Answer<\/button><\/p>\n<div class=\"hidden-div\" id=\"hidden-div\"><span style=\"\"><\/p>\n<div class=\"answer\">Correct Answer: <strong>AB<\/strong><\/div>\n<p><\/strong><\/span> <\/div>\n","protected":false},"excerpt":{"rendered":"<p>A financial company is trying to detect credit card fraud. The company observed that, on average, 2% of credit card transactions were fraudulent. A data scientist trained a classifier on a year&#039;s worth of credit card transactions data. The model needs to identify the fraudulent transactions (positives) from the regular ones (negatives). The company&#039;s goal [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[3,128],"class_list":["post-524","post","type-post","status-publish","format-standard","hentry","category-aws-certified-machine-learning-specialty-mls-c01","tag-aws-certified-machine-learning-specialty-mls-c01","tag-question-125"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/wp-json\/wp\/v2\/posts\/524","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/wp-json\/wp\/v2\/comments?post=524"}],"version-history":[{"count":0,"href":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/wp-json\/wp\/v2\/posts\/524\/revisions"}],"wp:attachment":[{"href":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/wp-json\/wp\/v2\/media?parent=524"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/wp-json\/wp\/v2\/categories?post=524"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/wp-json\/wp\/v2\/tags?post=524"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}