{"id":46754,"date":"2024-04-15T22:16:55","date_gmt":"2024-04-15T22:16:55","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=46754"},"modified":"2024-04-15T22:16:55","modified_gmt":"2024-04-15T22:16:55","slug":"imagine-you-are-solving-a-classification-problems-with-highly-imbalanced-class-the-majority-class-is-observed-99-of-times-in-the-training-data-your-model-has-99-accuracy-after-taking-the-prediction","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/imagine-you-are-solving-a-classification-problems-with-highly-imbalanced-class-the-majority-class-is-observed-99-of-times-in-the-training-data-your-model-has-99-accuracy-after-taking-the-prediction\/","title":{"rendered":"Imagine, you are solving a classification problems with highly imbalanced class. The majority class is observed 99% of times in the training data. Your model has 99% accuracy after taking the predictions on test data. Which of the following is true in such a case? 1. Accuracy metric is not a good idea for imbalanced class problems. 2.Accuracy metric is a good idea for imbalanced class problems. 3.Precision and recall metrics are good for imbalanced class problems. 4.Precision and recall metrics aren&#8217;t good for imbalanced class problems."},"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_6a97dbc3209e9\">\r\n                                            <div class=\"option\" data-option-key=\"option1\" data-is-correct=\"false\">\r\n                    1 and 3                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option2\" data-is-correct=\"false\">\r\n                    1 and 4                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option3\" data-is-correct=\"true\">\r\n                    2 and 3                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option4\" data-is-correct=\"false\">\r\n                    2 and 4                <\/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>C. 2 and 3<\/strong><\/p>\n<p>Accuracy is not a good idea for imbalanced class problems because it can be misleading. For example, if a model has 99% accuracy on a dataset where the majority class is observed 99% of times, it means that the model is simply always predicting the majority class. This is not a very useful model, because it will not be able to accurately predict the minority class.<\/p>\n<p>Precision and recall are better metrics for imbalanced class problems. Precision measures the fraction of predicted positives <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> that are actually positive, while recall measures the fraction of actual positives that are predicted positive. Both of these metrics are more informative than accuracy in the case of imbalanced class problems.<\/p>\n<p>Here is a more detailed explanation of each option:<\/p>\n<ul>\n<li>Option 1: Accuracy metric is not a good idea for imbalanced class problems. This <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> is because accuracy can be misleading in the case of imbalanced class problems. For example, if a model has 99% accuracy on a dataset where the majority class is observed 99% of times, it means that the model is simply always predicting the majority class. This is not a very useful model, because it will not be able to accurately predict the minority class.<\/li>\n<li>Option 2: Accuracy metric is a good idea for imbalanced class problems. This is not correct, as explained above.<\/li>\n<li>Option 3: Precision and recall metrics are good for imbalanced class problems. This is correct, as precision and recall are more informative than accuracy in the case of imbalanced class problems.<\/li>\n<li>Option 4: Precision and recall metrics aren&#8217;t good for imbalanced class problems. This is not correct, as explained above.<\/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>Imagine, you are solving a classification problems with highly imbalanced class. The majority class is observed 99% of times in the training data. Your model has 99% accuracy after taking the predictions on test data. Which of the following is true in such a case? 1. Accuracy metric is not a good idea for imbalanced class problems. 2.Accuracy metric is a good idea for imbalanced class problems. 3.Precision and recall metrics are good for imbalanced class problems. 4.Precision and recall metrics aren&#039;t good for imbalanced class problems.<\/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\/imagine-you-are-solving-a-classification-problems-with-highly-imbalanced-class-the-majority-class-is-observed-99-of-times-in-the-training-data-your-model-has-99-accuracy-after-taking-the-prediction\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Imagine, you are solving a classification problems with highly imbalanced class. The majority class is observed 99% of times in the training data. 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The majority class is observed 99% of times in the training data. Your model has 99% accuracy after taking the predictions on test data. Which of the following is true in such a case? 1. Accuracy metric is not a good idea for imbalanced class problems. 2.Accuracy metric is a good idea for imbalanced class problems. 3.Precision and recall metrics are good for imbalanced class problems. 4.Precision and recall metrics aren't good for imbalanced class problems.","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\/imagine-you-are-solving-a-classification-problems-with-highly-imbalanced-class-the-majority-class-is-observed-99-of-times-in-the-training-data-your-model-has-99-accuracy-after-taking-the-prediction\/","og_locale":"en_US","og_type":"article","og_title":"Imagine, you are solving a classification problems with highly imbalanced class. The majority class is observed 99% of times in the training data. Your model has 99% accuracy after taking the predictions on test data. 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