{"id":55833,"date":"2024-04-16T00:34:11","date_gmt":"2024-04-16T00:34:11","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=55833"},"modified":"2024-04-16T00:34:11","modified_gmt":"2024-04-16T00:34:11","slug":"in-svm-rbf-kernel-with-appropriate-parameters-to-perform-binary-classification-where-the-data-is-non-linearly-seperable-in-this-scenario","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/in-svm-rbf-kernel-with-appropriate-parameters-to-perform-binary-classification-where-the-data-is-non-linearly-seperable-in-this-scenario\/","title":{"rendered":"In SVM, RBF kernel with appropriate parameters to perform binary classification where the data is non-linearly seperable. In this scenario"},"content":{"rendered":"<p>[amp_mcq option1=&#8221;the decision boundry in the transformed feature space in non-linear&#8221; option2=&#8221;the decision boundry in the transformed feature space in linear&#8221; option3=&#8221;the decision boundry in the original feature space in not considered&#8221; option4=&#8221;the decision boundry in the original feature space in linear&#8221; correct=&#8221;option1&#8243;]<!--more--><\/p>\n<p>The correct answer is: A. the decision boundry in the transformed feature space in non-linear.<\/p>\n<p>In SVM, the RBF kernel is a non-linear kernel that can be used to map the data into a higher dimensional space where the data is linearly separable. In this scenario, the decision boundary in the transformed feature space will be non-linear.<\/p>\n<p>The other options are incorrect because:<\/p>\n<ul>\n<li>Option B is incorrect because the decision boundary in the transformed feature space is non-linear.<\/li>\n<li>Option C is incorrect because the decision boundary in the original feature space is not considered in SVM.<\/li>\n<li>Option D is incorrect because the decision boundary in the original feature space is linear.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>[amp_mcq option1=&#8221;the decision boundry in the transformed feature space in non-linear&#8221; option2=&#8221;the decision boundry in the transformed feature space in linear&#8221; option3=&#8221;the decision boundry in the original feature space in not considered&#8221; option4=&#8221;the decision boundry in the original feature space in linear&#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-55833","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>In SVM, RBF kernel with appropriate parameters to perform binary classification where the data is non-linearly seperable. In this scenario<\/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\/in-svm-rbf-kernel-with-appropriate-parameters-to-perform-binary-classification-where-the-data-is-non-linearly-seperable-in-this-scenario\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"In SVM, RBF kernel with appropriate parameters to perform binary classification where the data is non-linearly seperable. 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In this scenario","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\/in-svm-rbf-kernel-with-appropriate-parameters-to-perform-binary-classification-where-the-data-is-non-linearly-seperable-in-this-scenario\/","og_locale":"en_US","og_type":"article","og_title":"In SVM, RBF kernel with appropriate parameters to perform binary classification where the data is non-linearly seperable. 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