{"id":52568,"date":"2024-04-15T23:41:14","date_gmt":"2024-04-15T23:41:14","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=52568"},"modified":"2024-04-15T23:41:14","modified_gmt":"2024-04-15T23:41:14","slug":"what-is-the-primary-goal-of-dimensionality-reduction-techniques-like-principal-component-analysis-pca-in-machine-learning","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/what-is-the-primary-goal-of-dimensionality-reduction-techniques-like-principal-component-analysis-pca-in-machine-learning\/","title":{"rendered":"What is the primary goal of dimensionality reduction techniques like Principal Component Analysis (PCA) in machine learning?"},"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_6a97376a7e4e6\">\r\n                                            <div class=\"option\" data-option-key=\"option1\" data-is-correct=\"false\">\r\n                    To increase model interpretability                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option2\" data-is-correct=\"true\">\r\n                    To reduce the size of the training dataset                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option3\" data-is-correct=\"false\">\r\n                    To perform unsupervised learning                <\/div>\r\n                                            <div class=\"option\" data-option-key=\"option4\" data-is-correct=\"false\">\r\n                    To visualize data relationships                <\/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. To reduce the size of the training dataset.<\/strong><\/p>\n<p>Dimensionality reduction is the process of reducing the number of variables in a dataset while preserving as much of the information as possible. This can be useful for a number of reasons, such as:<\/p>\n<ul>\n<li>Reducing the computational cost of training and evaluating a model.<\/li>\n<li>Making it easier to visualize and understand the data.<\/li>\n<li>Making it possible to apply machine learning algorithms to datasets that would otherwise be too large.<\/li>\n<\/ul>\n<p>Principal component analysis (PCA) is a popular dimensionality reduction technique. It works by finding a set of orthogonal (i.e., uncorrelated) vectors, called principal components, that capture as much of the variance in the data as possible. The first principal component is the vector that captures 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> most variance, the second principal component captures the second most variance, and so on.<\/p>\n<p>Once the principal components have been found, the data can be projected onto them. This means that each data point is represented by a new vector, whose components are the projections of the data point onto the principal components. The number of principal components used to project the data can be chosen to trade off between the amount of information that is preserved and the computational cost of working with the projected data.<\/p>\n<p>PCA is a powerful <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> tool that can be used to reduce the size of training datasets, make it easier to visualize and understand data, and make it possible to apply machine learning algorithms to datasets that would otherwise be too large.<\/p>\n<p>Here is a brief explanation of each option:<\/p>\n<ul>\n<li><strong>A. To increase model interpretability<\/strong>. This is not the primary goal of dimensionality reduction techniques. While dimensionality reduction can sometimes make models easier to interpret, this is not its primary purpose.<\/li>\n<li><strong>C. To perform unsupervised learning<\/strong>. Dimensionality reduction is a supervised learning technique. It is used to reduce the dimensionality of data that has already been labeled. Unsupervised learning techniques, on the other hand, are used to learn from unlabeled data.<\/li>\n<li><strong>D. To visualize data relationships<\/strong>. Dimensionality reduction can be used to visualize data relationships, but this is not its primary purpose. The primary purpose of dimensionality reduction is to reduce the dimensionality of data.<\/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":[736],"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 primary goal of dimensionality reduction techniques like Principal Component Analysis (PCA) in machine learning?<\/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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