{"id":54843,"date":"2024-04-16T00:17:11","date_gmt":"2024-04-16T00:17:11","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=54843"},"modified":"2024-04-16T00:17:11","modified_gmt":"2024-04-16T00:17:11","slug":"is-the-most-drastic-one-and-should-be-considered-only-when-the-dataset-is-quite-large-the-number-of-missing-features-is-high-and-any-prediction-could-be-risky","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/is-the-most-drastic-one-and-should-be-considered-only-when-the-dataset-is-quite-large-the-number-of-missing-features-is-high-and-any-prediction-could-be-risky\/","title":{"rendered":". . . . . . . . is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky."},"content":{"rendered":"<p>[amp_mcq option1=&#8221;Removing the whole line&#8221; option2=&#8221;Creating sub-model to predict those features&#8221; option3=&#8221;Using an automatic strategy to input them according to the other known values&#8221; option4=&#8221;All above&#8221; correct=&#8221;option1&#8243;]<!--more--><\/p>\n<p>The correct answer is: <strong>A. Removing the whole line<\/strong>.<\/p>\n<p>Removing the whole line is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.<\/p>\n<ul>\n<li><strong>Creating sub-model to predict those features<\/strong> is a good option when the number of missing features is not too high and the dataset is not too large. This approach can help to improve the accuracy of the predictions.<\/li>\n<li><strong>Using an automatic strategy to input them according to the other known values<\/strong> is another good option when the number of missing features is not too high. This approach can help to reduce the number of missing values and improve the accuracy of the predictions.<\/li>\n<\/ul>\n<p>However, when the number of missing features is high, it is better to remove the whole line. This is because the predictions for the lines with missing features are likely to be inaccurate. Removing the lines with missing features will improve the accuracy of the overall predictions.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>[amp_mcq option1=&#8221;Removing the whole line&#8221; option2=&#8221;Creating sub-model to predict those features&#8221; option3=&#8221;Using an automatic strategy to input them according to the other known values&#8221; option4=&#8221;All above&#8221; correct=&#8221;option1&#8243;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[729],"tags":[],"class_list":["post-54843","post","type-post","status-publish","format-standard","hentry","category-machine-learning","no-featured-image-padding"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.2 (Yoast SEO v23.3) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>. . . . . . . . is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/exam.pscnotes.com\/mcq\/is-the-most-drastic-one-and-should-be-considered-only-when-the-dataset-is-quite-large-the-number-of-missing-features-is-high-and-any-prediction-could-be-risky\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\". . . . . . . . is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.\" \/>\n<meta property=\"og:description\" content=\"[amp_mcq option1=&#8221;Removing the whole line&#8221; option2=&#8221;Creating sub-model to predict those features&#8221; option3=&#8221;Using an automatic strategy to input them according to the other known values&#8221; option4=&#8221;All above&#8221; correct=&#8221;option1&#8243;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/exam.pscnotes.com\/mcq\/is-the-most-drastic-one-and-should-be-considered-only-when-the-dataset-is-quite-large-the-number-of-missing-features-is-high-and-any-prediction-could-be-risky\/\" \/>\n<meta property=\"og:site_name\" content=\"MCQ and Quiz for Exams\" \/>\n<meta property=\"article:published_time\" content=\"2024-04-16T00:17:11+00:00\" \/>\n<meta name=\"author\" content=\"rawan239\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"rawan239\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 minute\" \/>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":". . . . . . . . is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/exam.pscnotes.com\/mcq\/is-the-most-drastic-one-and-should-be-considered-only-when-the-dataset-is-quite-large-the-number-of-missing-features-is-high-and-any-prediction-could-be-risky\/","og_locale":"en_US","og_type":"article","og_title":". . . . . . . . is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.","og_description":"[amp_mcq option1=&#8221;Removing the whole line&#8221; option2=&#8221;Creating sub-model to predict those features&#8221; option3=&#8221;Using an automatic strategy to input them according to the other known values&#8221; option4=&#8221;All above&#8221; correct=&#8221;option1&#8243;]","og_url":"https:\/\/exam.pscnotes.com\/mcq\/is-the-most-drastic-one-and-should-be-considered-only-when-the-dataset-is-quite-large-the-number-of-missing-features-is-high-and-any-prediction-could-be-risky\/","og_site_name":"MCQ and Quiz for Exams","article_published_time":"2024-04-16T00:17:11+00:00","author":"rawan239","twitter_card":"summary_large_image","twitter_misc":{"Written by":"rawan239","Est. reading time":"1 minute"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/exam.pscnotes.com\/mcq\/is-the-most-drastic-one-and-should-be-considered-only-when-the-dataset-is-quite-large-the-number-of-missing-features-is-high-and-any-prediction-could-be-risky\/","url":"https:\/\/exam.pscnotes.com\/mcq\/is-the-most-drastic-one-and-should-be-considered-only-when-the-dataset-is-quite-large-the-number-of-missing-features-is-high-and-any-prediction-could-be-risky\/","name":". . . . . . . . is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.","isPartOf":{"@id":"https:\/\/exam.pscnotes.com\/mcq\/#website"},"datePublished":"2024-04-16T00:17:11+00:00","dateModified":"2024-04-16T00:17:11+00:00","author":{"@id":"https:\/\/exam.pscnotes.com\/mcq\/#\/schema\/person\/5807dafeb27d2ec82344d6cbd6c3d209"},"breadcrumb":{"@id":"https:\/\/exam.pscnotes.com\/mcq\/is-the-most-drastic-one-and-should-be-considered-only-when-the-dataset-is-quite-large-the-number-of-missing-features-is-high-and-any-prediction-could-be-risky\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/exam.pscnotes.com\/mcq\/is-the-most-drastic-one-and-should-be-considered-only-when-the-dataset-is-quite-large-the-number-of-missing-features-is-high-and-any-prediction-could-be-risky\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/exam.pscnotes.com\/mcq\/is-the-most-drastic-one-and-should-be-considered-only-when-the-dataset-is-quite-large-the-number-of-missing-features-is-high-and-any-prediction-could-be-risky\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/exam.pscnotes.com\/mcq\/"},{"@type":"ListItem","position":2,"name":"mcq","item":"https:\/\/exam.pscnotes.com\/mcq\/category\/mcq\/"},{"@type":"ListItem","position":3,"name":"Machine learning","item":"https:\/\/exam.pscnotes.com\/mcq\/category\/mcq\/machine-learning\/"},{"@type":"ListItem","position":4,"name":". . . . . . . . is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky."}]},{"@type":"WebSite","@id":"https:\/\/exam.pscnotes.com\/mcq\/#website","url":"https:\/\/exam.pscnotes.com\/mcq\/","name":"MCQ and Quiz for Exams","description":"","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/exam.pscnotes.com\/mcq\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-US"},{"@type":"Person","@id":"https:\/\/exam.pscnotes.com\/mcq\/#\/schema\/person\/5807dafeb27d2ec82344d6cbd6c3d209","name":"rawan239","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/exam.pscnotes.com\/mcq\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/761a7274f9cce048fa5b921221e7934820d74514df93ef195a9d22af0c1c9001?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/761a7274f9cce048fa5b921221e7934820d74514df93ef195a9d22af0c1c9001?s=96&d=mm&r=g","caption":"rawan239"},"sameAs":["https:\/\/exam.pscnotes.com"],"url":"https:\/\/exam.pscnotes.com\/mcq\/author\/rawan239\/"}]}},"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/exam.pscnotes.com\/mcq\/wp-json\/wp\/v2\/posts\/54843","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/exam.pscnotes.com\/mcq\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/exam.pscnotes.com\/mcq\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/exam.pscnotes.com\/mcq\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/exam.pscnotes.com\/mcq\/wp-json\/wp\/v2\/comments?post=54843"}],"version-history":[{"count":0,"href":"https:\/\/exam.pscnotes.com\/mcq\/wp-json\/wp\/v2\/posts\/54843\/revisions"}],"wp:attachment":[{"href":"https:\/\/exam.pscnotes.com\/mcq\/wp-json\/wp\/v2\/media?parent=54843"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/exam.pscnotes.com\/mcq\/wp-json\/wp\/v2\/categories?post=54843"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/exam.pscnotes.com\/mcq\/wp-json\/wp\/v2\/tags?post=54843"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}