{"id":8899,"date":"2018-02-21T12:09:14","date_gmt":"2018-02-21T12:09:14","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=8899"},"modified":"2025-07-27T20:05:47","modified_gmt":"2025-07-27T14:35:47","slug":"dimensionality-reduction-tutorial","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/dimensionality-reduction-tutorial\/","title":{"rendered":"What is Dimensionality Reduction – Techniques, Methods, Components"},"content":{"rendered":"
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In this Machine Learning Tutorial<\/strong><\/a>, we will study What is Dimensionality Reduction. Also, will cover every related aspect of machine learning- Dimensionality Reduction like components & Methods of Dimensionality Reduction, Principle Component analysis & Importance of Dimensionality Reduction, Feature selection, Advantages & Disadvantages of Dimensionality Reduction. Along with this, we will see all W’s of Dimensionality Reduction.<\/p>\n<\/div>\n

So, let’s start Dimensionality Reduction Tutorial.<\/p>\n

What is Dimensionality Reduction?<\/h3>\n
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In machine learning<\/strong> we are having too many factors on which the final classification is done<\/span>. These factors are basically<\/span>,\u00a0known<\/span> as variables. The higher the number of features, the harder it gets to visualize the training set and then work on it. Sometimes, most of these features are correlated<\/span>, and hence redundant. This is where dimensionality reduction algorithms come into play.<\/div>\n<\/div>\n
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Motivation<\/h3>\n<\/div>\n
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