{"id":45103,"date":"2024-04-15T21:53:16","date_gmt":"2024-04-15T21:53:16","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=45103"},"modified":"2024-04-15T21:53:16","modified_gmt":"2024-04-15T21:53:16","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-model","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-model\/","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_6a97dd9c17e89\">\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=\"true\">\r\n                    ii and iii                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option3\" data-is-correct=\"false\">\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: <strong>B. ii and iii<\/strong><\/p>\n<p>Models which overfit are more likely to have high variance and low bias.<\/p>\n<p>Bias is the difference between <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> the expected value of the model&#8217;s predictions and the true value. Variance is the spread of the model&#8217;s predictions around the expected value.<\/p>\n<p>A model with high bias will tend to make the same mistake over and over again, while a model with high variance will make different mistakes each time.<\/p>\n<p>Overfitting occurs when a model learns the training data too well and is not able to generalize to new data. This can happen when the model is too complex or when the training data is too small.<\/p>\n<p>When a model overfits, it will have high variance and low bias. This is because the model will have learned the noise in the training data and will not be able to generalize to new data.<\/p>\n<p>To avoid overfitting, it is important to use a model that is not too complex and to train the model on a large enough dataset.<\/p>\n<p>Here is a <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> more detailed explanation of each option:<\/p>\n<ul>\n<li>Option (i): Models which overfit are more likely to have high bias.<\/li>\n<\/ul>\n<p>This is not true. Models which overfit are more likely to have high variance and low bias.<\/p>\n<ul>\n<li>Option (ii): Models which overfit are more likely to have low bias.<\/li>\n<\/ul>\n<p>This is true. Models which overfit are more likely to have low bias.<\/p>\n<ul>\n<li>Option (iii): Models which overfit are more likely to have high variance.<\/li>\n<\/ul>\n<p>This is true. Models which overfit are more likely to have high variance.<\/p>\n<ul>\n<li>Option (iv): Models which overfit are more likely to have low variance.<\/li>\n<\/ul>\n<p>This is not true. Models which overfit are more likely to have high variance.<\/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>Regarding bias and variance, which of the following statements are true? (Here &#039;high&#039; and &#039;low&#039; 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. 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