{"id":47108,"date":"2024-04-15T22:22:03","date_gmt":"2024-04-15T22:22:03","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=47108"},"modified":"2024-04-15T22:22:03","modified_gmt":"2024-04-15T22:22:03","slug":"suppose-you-are-building-a-svm-model-on-data-x-the-data-x-can-be-error-prone-which-means-that-you-should-not-trust-any-specific-data-point-too-much-now-think-that-you-want-to-build-a-svm-model-which","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/suppose-you-are-building-a-svm-model-on-data-x-the-data-x-can-be-error-prone-which-means-that-you-should-not-trust-any-specific-data-point-too-much-now-think-that-you-want-to-build-a-svm-model-which\/","title":{"rendered":"Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of it&#8217;s hyper parameter.What would happen when you use very large value of C(C->infinity)?"},"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_6a9796ad0030e\">\r\n                                            <div class=\"option\" data-option-key=\"option1\" data-is-correct=\"true\">\r\n                    We can still classify 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 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                    Can&#039;t Say                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option4\" data-is-correct=\"false\">\r\n                    None of these                <\/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: <strong>A. We can still classify data correctly for given setting of hyper parameter C<\/strong>.<\/p>\n<p>When the value of C is very large, the SVM model will try to classify all data points correctly, even if some of them are error prone. This is because the model will not want to sacrifice any data points, even if they are likely to be incorrect. As a result, the model may overfit the data and make poor predictions on new data.<\/p>\n<p>However, if the data is very clean and there are no error prone data points, then using a very large value of C may be beneficial. This is because the model will be able to learn the true relationship between the features and the labels, and will be able to make accurate predictions on new data.<\/p>\n<p>In general, it is important to choose a value of C that is appropriate for the data. If the data is error prone, then a smaller value of C should be used. If the data is clean, then a larger value of C may be beneficial.<\/p>\n<p>Here is a brief explanation of each option:<\/p>\n<ul>\n<li>Option A: We can still classify 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 data correctly for given setting of hyper parameter C. This is not the correct answer, as the model will still be able to classify some data points correctly, even if the value of C is very large.<\/li>\n<li>Option C: Can&#8217;t Say. This is not the correct answer, as it is possible to classify data correctly for a given setting of hyper parameter C.<\/li>\n<li>Option D: <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                    <path d=\"M549.7 124.1c-6.3-23.7-24.8-42.3-48.3-48.6C458.8 64 <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> 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> None of these. This is not the correct answer, as Option A is the correct answer.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Join Our Telegram Channel 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>Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. 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The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of it's hyper parameter.What would happen when you use very large value of C(C->infinity)?","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\/suppose-you-are-building-a-svm-model-on-data-x-the-data-x-can-be-error-prone-which-means-that-you-should-not-trust-any-specific-data-point-too-much-now-think-that-you-want-to-build-a-svm-model-which\/","og_locale":"en_US","og_type":"article","og_title":"Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. 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