{"id":54154,"date":"2024-04-16T00:05:21","date_gmt":"2024-04-16T00:05:21","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=54154"},"modified":"2024-04-16T00:05:21","modified_gmt":"2024-04-16T00:05:21","slug":"suppose-you-are-given-n-predictions-on-test-data-by-n-different-models-m1-m2-mn-respectively-which-of-the-following-methods-can-be-used-to-combine-the-predictions-of-these-models","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/suppose-you-are-given-n-predictions-on-test-data-by-n-different-models-m1-m2-mn-respectively-which-of-the-following-methods-can-be-used-to-combine-the-predictions-of-these-models\/","title":{"rendered":"Suppose you are given &#8216;n&#8217; predictions on test data by &#8216;n&#8217; different models (M1, M2, &#8230;. Mn) respectively. Which of the following method(s) can be used to combine the predictions of these models? Note: We are working on a regression problem 1. Median 2. Product 3. Average 4. Weighted sum 5. Minimum and Maximum 6. Generalized mean rule"},"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_6a97605f6701a\">\r\n                                            <div class=\"option\" data-option-key=\"option1\" data-is-correct=\"false\">\r\n                    1, 3 and 4                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option2\" data-is-correct=\"false\">\r\n                    1,3 and 6                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option3\" data-is-correct=\"false\">\r\n                    1,3, 4 and 6                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option4\" data-is-correct=\"true\">\r\n                    all of above                <\/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>D. all of above<\/strong><\/p>\n<p>The median, average, weighted sum, minimum, maximum, and generalized mean rule are all methods that can be used to combine the predictions of multiple models.<\/p>\n<p>The median is the middle value in a sorted list of numbers. It is a robust measure of central tendency that is not affected by outliers. The average is the sum of a set of numbers divided <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> by the number of numbers in the set. It is a simple and intuitive measure of central tendency, but it can be sensitive to outliers. The weighted sum is the sum of a set of numbers, each of which is multiplied by a weight. The weights can be used to give more importance to some numbers than others. The minimum is the smallest number in a set. The maximum is the largest number in a set. The generalized mean rule is a method for combining the predictions of multiple models that takes into account the uncertainty in the predictions.<\/p>\n<p>In the context of a regression problem, the goal is to predict a continuous value. The median, average, weighted sum, minimum, maximum, and generalized mean rule can all be used to combine the predictions of multiple models to produce a single prediction. The choice of method will depend on the specific application.<\/p>\n<p>For example, if the goal is to produce a robust prediction that is not affected by outliers, then the median may be a good choice. If the <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> goal is to produce a prediction that is as accurate as possible, then the average may be a good choice. If the goal is to produce a prediction that is tailored to the specific application, then the weighted sum or generalized mean rule may be a good choice.<\/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>Suppose you are given &#039;n&#039; predictions on test data by &#039;n&#039; different models (M1, M2, .... Mn) respectively. Which of the following method(s) can be used to combine the predictions of these models? Note: We are working on a regression problem 1. Median 2. Product 3. Average 4. Weighted sum 5. Minimum and Maximum 6. 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