{"id":51977,"date":"2024-04-15T23:32:33","date_gmt":"2024-04-15T23:32:33","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=51977"},"modified":"2024-04-15T23:32:33","modified_gmt":"2024-04-15T23:32:33","slug":"which-of-the-following-can-help-to-reduce-overfitting-in-an-svm-classifier","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/which-of-the-following-can-help-to-reduce-overfitting-in-an-svm-classifier\/","title":{"rendered":"Which of the following can help to reduce overfitting in an SVM classifier?"},"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_6a97a2b450913\">\r\n                                            <div class=\"option\" data-option-key=\"option1\" data-is-correct=\"true\">\r\n                    use of slack variables                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option2\" data-is-correct=\"false\">\r\n                    high-degree polynomial features                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option3\" data-is-correct=\"false\">\r\n                    normalizing the data                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option4\" data-is-correct=\"false\">\r\n                    setting a very low learning rate                <\/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. use of slack variables<\/strong>.<\/p>\n<p>Slack variables are a technique used in SVMs to reduce overfitting. They allow the model to make some mistakes on the training data, in order to generalize better to new data. This is done by adding a penalty to the loss function for each misclassified example. The penalty is larger for examples that are far from the decision boundary, so this encourages the model to classify these examples correctly.<\/p>\n<p>High-degree polynomial features can also help to reduce overfitting, but they can also make the model more complex and difficult to train. Normalizing the data can also help to reduce overfitting, but it is not as effective as using slack variables. Setting a very low learning rate can also help to reduce overfitting, but it can also make the model more slow <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 64 288 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> to train.<\/p>\n<p>Here is a more detailed explanation of each option:<\/p>\n<ul>\n<li>\n<p><strong>A. use of slack variables<\/strong>. Slack variables are a technique used in SVMs to reduce overfitting. They allow the model to make some mistakes on the training data, in order to generalize better to new data. This is done by adding a penalty to the loss function for each misclassified example. The penalty is larger for examples that are far from the decision boundary, so this encourages the model to classify these examples correctly.<\/p>\n<\/li>\n<li>\n<p><strong>B. high-degree polynomial features<\/strong>. High-degree polynomial features can also help to reduce overfitting, but they can also make the model more complex and difficult to train. This is because high-degree polynomials can fit the training data very well, but they may not generalize well to new data.<\/p>\n<\/li>\n<li>\n<p><strong>C. normalizing the data<\/strong>. Normalizing the data can also help to reduce overfitting, but it is not as effective as using slack variables. This is because normalizing the data does not change the shape of the decision boundary, but it can help to make the model more robust to noise in the data.<\/p>\n<\/li>\n<li>\n<p><strong>D. setting a very low learning rate<\/strong>. Setting a very low learning rate can also help to reduce overfitting, but it can also make the model more slow to train. This is because a low learning rate means that the model will take more steps to converge to a <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> solution.<\/p>\n<\/li>\n<\/ul>\n<p>In conclusion, the correct answer is: <strong>A. use of slack variables<\/strong>. Slack variables are the most effective way to reduce overfitting in SVMs.<\/p>\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>Which of the following can help to reduce overfitting in an SVM classifier?<\/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\/which-of-the-following-can-help-to-reduce-overfitting-in-an-svm-classifier\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" 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