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<!DOCTYPE html> <html> <head> <title>HTML, CSS and JavaScript demo</title> </head> <body> <!-- Start your code here --> <h1>Dimensionality Reduction in Data Science</h1> <p> In the field of data science, dimensionality reduction is a common technique used to extract important features from a dataset. It involves reducing the number of input variables while preserving the majority of the variability present in the data. This technique is particularly useful in situations where the number of input variables is large, which can lead to high computational costs and poor performance of machine learning models. In this article, we will discuss the basics of dimensionality reduction in data science, including the types of techniques used, their benefits, and some common applications. What is Dimensionality Reduction? Dimensionality reduction is a process of reducing the number of input variables in a dataset. This process involves selecting a subset of the original variables or transforming them into a smaller set of variables that still preserves the most important information from the original data. The goal of dimensionality reduction is to remove redundant or irrelevant information from the data, which can improve the performance of machine learning models, reduce computational costs, and increase the interpretability of the results. There are two main types of dimensionality reduction techniques: feature selection and feature extraction. Feature Selection Feature selection is a technique that involves selecting a subset of the original variables in the dataset. This technique involves evaluating the importance of each feature and selecting the most informative ones based on a predefined criterion. The criterion can be based on statistical measures such as correlation, mutual information, or the chi-squared test, or it can be based on domain knowledge and expertise. The benefits of feature selection are that it can reduce the computational costs of machine learning models and improve their interpretability. However, it may not always be the best choice as it can lead to loss of important information, and it can be difficult to select the most informative features. </p> <a href="https://hrushi20002506.rajce.idnes.cz/profil/informace">Data Science Classes in Pune</a><br/> <a href="https://hrushi20002506.rajce.idnes.cz/profil/informace">Data Science Course in Pune</a><br/> <a href="https://hrushi20002506.rajce.idnes.cz/profil/informace">Online Data Science Training in Pune</a><br/> <!-- End your code here --> </body> </html>
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