{"id":47021,"date":"2024-04-15T22:20:48","date_gmt":"2024-04-15T22:20:48","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=47021"},"modified":"2024-04-15T22:20:48","modified_gmt":"2024-04-15T22:20:48","slug":"in-svm-which-has-quadratic-kernel-function-of-polynomial-degree-2-that-has-slack-variable-c-as-one-hyper-paramenter-what-would-happen-if-we-use-very-large-value-for-c","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/in-svm-which-has-quadratic-kernel-function-of-polynomial-degree-2-that-has-slack-variable-c-as-one-hyper-paramenter-what-would-happen-if-we-use-very-large-value-for-c\/","title":{"rendered":"In SVM which has quadratic kernel function of polynomial degree 2 that has slack variable C as one hyper paramenter. What would happen if we use very large value for C"},"content":{"rendered":"<p>\r\n    <!-- Check if it's an AMP page -->\r\n            <!-- Non-AMP version -->\r\n        <div class=\"mcq-container\" data-quiz-id=\"quizState_6a9767617085e\">\r\n                                            <div class=\"option\" data-option-key=\"option1\" data-is-correct=\"true\">\r\n                    we can still classify the data correctly for given setting of hyper parameter c                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option2\" data-is-correct=\"false\">\r\n                    we can not classify the data correctly for given setting of hyper parameter c                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option3\" data-is-correct=\"false\">\r\n                    we can not classify the data at all                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option4\" data-is-correct=\"false\">\r\n                    data can be classified correctly without any impact of c                <\/div>\r\n                            \r\n            <!-- Feedback messages for non-AMP -->\r\n            <div class=\"feedback\" data-feedback=\"wrong\">Answer is Right!<\/div>\r\n            <div class=\"feedback\" data-feedback=\"right\">Answer is Wrong!<\/div>\r\n        <\/div>\r\n\r\n        <script>\r\n        document.addEventListener('DOMContentLoaded', function () {\r\n            var containers = document.querySelectorAll('.mcq-container');\r\n\r\n            containers.forEach(function(container) {\r\n                var options = container.querySelectorAll('.option');\r\n                var feedbackSelect = container.querySelector('[data-feedback=\"select\"]');\r\n                var feedbackWrong = container.querySelector('[data-feedback=\"wrong\"]');\r\n                var feedbackRight = container.querySelector('[data-feedback=\"right\"]');\r\n\r\n                options.forEach(function(option) {\r\n                    option.addEventListener('click', function() {\r\n                        var selectedOption = option.getAttribute('data-option-key');\r\n                        var isCorrect = option.getAttribute('data-is-correct') === 'true';\r\n\r\n                        \/\/ Remove previous selections\r\n                        options.forEach(function(opt) {\r\n                            opt.classList.remove('correct', 'incorrect');\r\n                        });\r\n\r\n                        \/\/ Add the correct\/incorrect class\r\n                        if (isCorrect) {\r\n                            option.classList.add('correct');\r\n                            feedbackRight.hidden = false;\r\n                            feedbackWrong.hidden = true;\r\n                        } else {\r\n                            option.classList.add('incorrect');\r\n                            feedbackRight.hidden = true;\r\n                            feedbackWrong.hidden = false;\r\n                        }\r\n\r\n                        \/\/ Hide select feedback\r\n                        feedbackSelect.hidden = true;\r\n                    });\r\n                });\r\n            });\r\n        });\r\n        <\/script>\r\n    \r\n    <!--more--><\/p>\n<p>The correct answer is: A. we can still classify the data correctly for given setting of hyper parameter c.<\/p>\n<p>The slack variable $C$ is a hyperparameter in support vector machines (SVMs) that controls the trade-off between maximizing the margin and minimizing the number of misclassifications. A larger value of $C$ will result in a smaller number of misclassifications, but at the cost of a smaller margin.<\/p>\n<p>If we use a very large value for $C$, then we will minimize the number of misclassifications at all costs. This means that we will allow some of the data points to be misclassified, but we will ensure that the margin is as large as possible.<\/p>\n<p>In practice, it is often not necessary to use a very large value for $C$. A value of $C$ that is too large can result in overfitting, which can lead to poor generalization performance.<\/p>\n<p>Here is a brief explanation of each option:<\/p>\n<ul>\n<li>Option A: We can still classify the data correctly for given setting of hyper parameter c. This is the correct answer, as explained above.<\/li>\n<li>Option B: We can not classify the data correctly for given setting of hyper parameter c. This is not correct, as we can still classify the data correctly even if we use a very large value for $C$.<\/li>\n<li>Option C: We can not classify the data at all. This is not correct, as we can always classify <div class=\"youtube-subscribe-container\">\r\n        <a href=\"https:\/\/www.youtube.com\/channel\/UCNHT8lW-JmLC68rjBfZhdkg?sub_confirmation=1\" target=\"_blank\" class=\"youtube-subscribe-button\">\r\n            <span class=\"youtube-icon\">\r\n                <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 576 512\">\r\n                    <div class=\"telegram-channel-container\">\r\n        <a href=\"https:\/\/t.me\/pscnotes2025\" target=\"_blank\" class=\"telegram-channel-button\">\r\n            <span class=\"telegram-icon\">\r\n                <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 496 512\">\r\n                    <path fill=\"white\" d=\"M248,8C111,8,0,119,0,256s111,248,248,248s248-111,248-248S385,8,248,8z M362,177L320,367c-3,14-10,18-20,14l-56-41l-27,26 c-3,3-5,5-10,5l4-63L323,196c5-5-1-7-8-3l-98,62l-42-13c-9-3-10-9,2-14l162-63C351,160,365,164,362,177z\"\/>\r\n                <\/svg>\r\n            <\/span>\r\n            Join Our Telegram Channel\r\n        <\/a>\r\n    <\/div> <path d=\"M549.7 124.1c-6.3-23.7-24.8-42.3-48.3-48.6C458.8 64 288 64 288 64S117.2 64 74.6 75.5c-23.5 6.3-42 24.9-48.3 48.6-11.4 42.9-11.4 132.3-11.4 132.3s0 89.4 11.4 132.3c6.3 23.7 24.8 41.5 48.3 47.8C117.2 448 288 448 288 448s170.8 0 213.4-11.5c23.5-6.3 42-24.2 48.3-47.8 11.4-42.9 11.4-132.3 11.4-132.3s0-89.4-11.4-132.3zm-317.5 213.5V175.2l142.7 81.2-142.7 81.2z\"\/>\r\n                <\/svg>\r\n            <\/span>\r\n            Subscribe on YouTube\r\n        <\/a>\r\n    <\/div> the data correctly if we use a large enough value for $C$.<\/li>\n<li>Option D: Data can be classified correctly without any impact of c. This is not correct, as the value of $C$ does have an impact on the classification performance of SVMs.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Subscribe on YouTube<\/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":[],"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 which has quadratic kernel function of polynomial degree 2 that has slack variable C as one hyper paramenter. 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