{"id":59496,"date":"2024-04-16T01:38:15","date_gmt":"2024-04-16T01:38:15","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=59496"},"modified":"2024-04-16T01:38:15","modified_gmt":"2024-04-16T01:38:15","slug":"what-is-the-term-for-the-process-of-reducing-the-dimensionality-of-data-while-retaining-its-most-important-features","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/what-is-the-term-for-the-process-of-reducing-the-dimensionality-of-data-while-retaining-its-most-important-features\/","title":{"rendered":"What is the term for the process of reducing the dimensionality of data while retaining its most important features?"},"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_6a974678dd4bd\">\r\n                                            <div class=\"option\" data-option-key=\"option1\" data-is-correct=\"false\">\r\n                    Data Imputation                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option2\" data-is-correct=\"false\">\r\n                    Data Scaling                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option3\" data-is-correct=\"true\">\r\n                    Data Reduction                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option4\" data-is-correct=\"false\">\r\n                    Data Normalization                <\/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. Data Reduction<\/strong>.<\/p>\n<p>Data reduction is the process of reducing the dimensionality of data while retaining its most important features. This can be done through a variety of methods, such as principal component analysis (PCA), factor analysis, and t-distributed stochastic neighbor embedding (t-SNE).<\/p>\n<p>Data imputation is the process of filling in missing values in a dataset. This can be done through a variety of methods, such as mean imputation, median imputation, and multiple imputation.<\/p>\n<p>Data scaling is the process of transforming data so that it has a common scale. This can be done through a variety of methods, such as min-max scaling, z-score scaling, and rank-based scaling.<\/p>\n<p>Data normalization is the process of transforming data so that it has a mean of 0 and a standard deviation of 1. This can be done through a variety of methods, such as mean-variance normalization, min-max normalization, and z-score normalization.<\/p>\n<p>Here are some additional details about each of the options:<\/p>\n<ul>\n<li><strong>Data Imputation<\/strong> is the process of filling in missing values in a dataset. This can be done through a variety of methods, such as mean imputation, median imputation, and multiple imputation. Mean imputation is the simplest method, and it involves replacing each missing value with the mean of the non-missing values. Median imputation is similar to mean imputation, but it replaces each missing value with the median of the non-missing values. Multiple imputation is a more sophisticated method, and it involves creating multiple datasets with the missing values replaced by different values. The results from the multiple datasets are then combined to produce a more accurate estimate of the missing values.<\/li>\n<li><strong>Data Scaling<\/strong> is the process of transforming data so that it has a common <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> scale. This can be done through a variety of methods, such as min-max scaling, z-score scaling, and rank-based scaling. Min-max scaling involves rescaling the data so that the minimum value is <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> 0 and the maximum value is 1. Z-score scaling involves rescaling the data so that the mean is 0 and the standard deviation is 1. Rank-based scaling involves rescaling the data so that the ranks of the data points are preserved.<\/li>\n<li><strong>Data Reduction<\/strong> is the process of reducing the dimensionality of data while retaining its most important features. This can be done through a variety of methods, such as principal component analysis (PCA), factor analysis, and t-distributed stochastic neighbor embedding (t-SNE). Principal component analysis (PCA) is a method that projects the data onto a lower-dimensional space while preserving as much of the variance in the data as possible. Factor analysis is a method that identifies a set of latent factors that explain the covariance structure of the data. T-distributed stochastic neighbor embedding (t-SNE) is a method that visualizes high-dimensional data in a low-dimensional space.<\/li>\n<\/ul>\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":[735],"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>What is the term for the process of reducing the dimensionality of data while retaining its most important features?<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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