{"id":59132,"date":"2024-04-16T01:31:48","date_gmt":"2024-04-16T01:31:48","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=59132"},"modified":"2024-04-16T01:31:48","modified_gmt":"2024-04-16T01:31:48","slug":"regarding-bias-and-variance-which-of-the-following-statements-are-true-here-high-and-low-are-relative-to-the-ideal-model-i-models-which-overfit-are-more-likely-to-have-high-bias-ii-mod","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/regarding-bias-and-variance-which-of-the-following-statements-are-true-here-high-and-low-are-relative-to-the-ideal-model-i-models-which-overfit-are-more-likely-to-have-high-bias-ii-mod\/","title":{"rendered":"Regarding bias and variance, which of the following statements are true? (Here &#8216;high&#8217; and &#8216;low&#8217; are relative to the ideal model. (i) Models which overfit are more likely to have high bias (ii) Models which overfit are more likely to have low bias (iii) Models which overfit are more likely to have high variance (iv) Models which overfit are more likely to have low variance"},"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_6a974be335d93\">\r\n                                            <div class=\"option\" data-option-key=\"option1\" data-is-correct=\"false\">\r\n                    (i) and (ii)                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option2\" data-is-correct=\"false\">\r\n                    (ii) and (iii)                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option3\" data-is-correct=\"true\">\r\n                    (iii) and (iv)                <\/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: (iii) Models which overfit are more likely to have high variance.<\/p>\n<p>Bias and variance are two key concepts in machine learning. Bias refers to the error made by a model due to its systematic underestimation or overestimation of the true value. Variance refers to the error made by a model due to its sensitivity to small changes in the training data.<\/p>\n<p>Overfitting occurs when a model learns the training data too well and does not generalize well to new data. This can happen when a model is too complex or when the training data is too small. Overfitting can be avoided by using regularization techniques, such as L2 regularization or dropout.<\/p>\n<p>Models which overfit are more likely to have high variance because they are too sensitive to small changes in the training data. This means that they will perform well on the training data but poorly on new data.<\/p>\n<p>Here is a more detailed explanation of each option:<\/p>\n<p>(i) Models which overfit are more likely to have high bias. This is not true. Overfitting is caused by a model learning the training data too well, which leads to high variance. High bias is caused by <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> a model not learning the training data well enough, which leads to low variance.<\/p>\n<p>(ii) Models which overfit are more likely to have low bias. This is also not true. Overfitting is caused by a model learning the training data too well, which leads to high variance. Low bias is caused by a model learning the training data well enough, which leads to low variance.<\/p>\n<p>(iii) Models which overfit are more likely to have high variance. This is true. Overfitting is caused by a model learning the training data too well, which leads to high variance.<\/p>\n<p>(iv) Models which overfit are more likely to have low variance. This is not true. Overfitting is caused by a model learning the training data too well, which leads to high variance. Low <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> variance is caused by a model learning the training data well enough, which leads to low variance.<\/p>\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>Regarding bias and variance, which of the following statements are true? 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