{"id":53192,"date":"2024-04-15T23:50:25","date_gmt":"2024-04-15T23:50:25","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=53192"},"modified":"2024-04-15T23:50:25","modified_gmt":"2024-04-15T23:50:25","slug":"what-characterize-is-hyperplance-in-geometrical-model-of-machine-learning","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/what-characterize-is-hyperplance-in-geometrical-model-of-machine-learning\/","title":{"rendered":"What characterize is hyperplance in geometrical model of machine learning?"},"content":{"rendered":"<p>[amp_mcq option1=&#8221;a plane with 1 dimensional fewer than number of input attributes&#8221; option2=&#8221;a plane with 2 dimensional fewer than number of input attributes&#8221; option3=&#8221;a plane with 1 dimensional more than number of input attributes&#8221; option4=&#8221;a plane with 2 dimensional more than number of input attributes&#8221; correct=&#8221;option1&#8243;]<!--more--><\/p>\n<p>The correct answer is: A. a plane with 1 dimensional fewer than number of input attributes.<\/p>\n<p>A hyperplane is a flat surface in a higher-dimensional space. In a geometrical model of machine learning, a hyperplane is a plane that separates the data points into two classes. The number of input attributes is the number of dimensions in the space. A hyperplane with 1 dimensional fewer than number of input attributes is a plane that separates the data points into two classes in a space with one dimension fewer than the number of input attributes. This is the most common type of hyperplane used in machine learning.<\/p>\n<p>Option B is incorrect because a plane with 2 dimensional fewer than number of input attributes would be a line. A line is not a flat surface, so it cannot be used to separate data points into two classes.<\/p>\n<p>Option C is incorrect because a plane with 1 dimensional more than number of input attributes would be a plane that separates the data points into two classes in a space with one dimension more than the number of input attributes. This is not a common type of hyperplane used in machine learning.<\/p>\n<p>Option D is incorrect because a plane with 2 dimensional more than number of input attributes would be a plane that separates the data points into two classes in a space with two dimensions more than the number of input attributes. This is not a common type of hyperplane used in machine learning.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>[amp_mcq option1=&#8221;a plane with 1 dimensional fewer than number of input attributes&#8221; option2=&#8221;a plane with 2 dimensional fewer than number of input attributes&#8221; option3=&#8221;a plane with 1 dimensional more than number of input attributes&#8221; option4=&#8221;a plane with 2 dimensional more than number of input attributes&#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-53192","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>What characterize is hyperplance in geometrical model of machine learning?<\/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\/what-characterize-is-hyperplance-in-geometrical-model-of-machine-learning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What characterize is hyperplance in geometrical model of machine learning?\" \/>\n<meta property=\"og:description\" content=\"[amp_mcq option1=&#8221;a plane with 1 dimensional fewer than number of input attributes&#8221; 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