[amp_mcq option1=”Data Augmentation” option2=”Data Imputation” option3=”Data Transformation” option4=”Data Normalization” correct=”option2″]<\/p>\n
The correct answer is B. Data Imputation.<\/p>\n
Data imputation is a technique used to fill in missing values in a dataset. This can be done by using a variety of methods, such as mean imputation, median imputation, or multiple imputation.<\/p>\n
Data augmentation is a technique used to increase the size of a dataset by creating new data points that are similar to the existing data points. This can be done by using a variety of methods, such as data generation, data synthesis, or data augmentation with noise.<\/p>\n
Data transformation is a technique used to change the form of the data in a dataset. This can be done by using a variety of methods, such as normalization, standardization, or feature scaling.<\/p>\n
Data normalization is a technique used to make the data in a dataset have a mean of 0 and a standard deviation of 1. This can be done by using a variety of methods, such as z-score normalization or min-max normalization.<\/p>\n","protected":false},"excerpt":{"rendered":"
[amp_mcq option1=”Data Augmentation” option2=”Data Imputation” option3=”Data Transformation” option4=”Data Normalization” correct=”option2″]<\/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-47384","post","type-post","status-publish","format-standard","hentry","category-introduction-to-data-science","no-featured-image-padding"],"yoast_head":"\n