[amp_mcq option1=”Dimensionality Reduction” option2=”Data Cleaning” option3=”Feature Engineering” option4=”Data Transformation” correct=”option1″]<\/p>\n
The correct answer is: A. Dimensionality Reduction<\/strong><\/p>\n Dimensionality reduction is the process of reducing the number of features in a dataset while preserving as much information as possible. This can be done in a number of ways, such as principal component analysis (PCA), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE).<\/p>\n Data cleaning is the process of identifying and correcting errors in data. This can be done by removing duplicate records, correcting typos, and filling in missing values.<\/p>\n Feature engineering is the process of creating new features from existing features. This can be done by combining existing features, transforming existing features, or creating new features from scratch.<\/p>\n Data transformation is the process of changing the format of data. This can be done by converting data from one type to another, such as from text to numeric, or by reshaping data into a different format.<\/p>\n In conclusion, dimensionality reduction is the process of reducing the number of features in a dataset while preserving as much information as possible. This can be done in a number of ways, such as principal component analysis (PCA), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE).<\/p>\n","protected":false},"excerpt":{"rendered":" [amp_mcq option1=”Dimensionality Reduction” option2=”Data Cleaning” option3=”Feature Engineering” option4=”Data Transformation” correct=”option1″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[734],"tags":[],"class_list":["post-53741","post","type-post","status-publish","format-standard","hentry","category-introduction-to-data-science","no-featured-image-padding"],"yoast_head":"\n