{"id":51360,"date":"2024-04-15T23:23:42","date_gmt":"2024-04-15T23:23:42","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=51360"},"modified":"2024-04-15T23:23:42","modified_gmt":"2024-04-15T23:23:42","slug":"suppose-you-are-using-rbf-kernel-in-svm-with-high-gamma-value-what-does-this-signify","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/suppose-you-are-using-rbf-kernel-in-svm-with-high-gamma-value-what-does-this-signify\/","title":{"rendered":"Suppose you are using RBF kernel in SVM with high Gamma value. What does this signify?"},"content":{"rendered":"<p>[amp_mcq option1=&#8221;The model would consider even far away points from hyperplane for modeling&#8221; option2=&#8221;The model would consider only the points close to the hyperplane for modeling&#8221; option3=&#8221;The model would not be affected by distance of points from hyperplane for modeling&#8221; option4=&#8221;None of the above&#8221; correct=&#8221;option1&#8243;]<!--more--><\/p>\n<p>The correct answer is: A. The model would consider even far away points from hyperplane for modeling.<\/p>\n<p>The RBF kernel is a type of kernel function that is often used in support vector machines (SVMs). It is a radial basis function, which means that it is centered at a particular point and has a Gaussian shape. The gamma parameter controls the width of the Gaussian function. A higher gamma value corresponds to a narrower Gaussian function, which means that the model will be more sensitive to points that are close to the center of the function. In other words, a higher gamma value will cause the model to consider even far away points from the hyperplane for modeling.<\/p>\n<p>Option B is incorrect because the RBF kernel does not consider only the points close to the hyperplane for modeling. Option C is incorrect because the RBF kernel is affected by the distance of points from the hyperplane for modeling. Option D is incorrect because the RBF kernel is a type of kernel function that is often used in SVMs.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>[amp_mcq option1=&#8221;The model would consider even far away points from hyperplane for modeling&#8221; option2=&#8221;The model would consider only the points close to the hyperplane for modeling&#8221; option3=&#8221;The model would not be affected by distance of points from hyperplane for modeling&#8221; option4=&#8221;None of the 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-51360","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>Suppose you are using RBF kernel in SVM with high Gamma value. 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