[amp_mcq option1=”Data visualization” option2=”Data augmentation” option3=”Feature engineering” option4=”Data partitioning” correct=”option4″]<\/p>\n
The correct answer is: D. Data partitioning<\/strong><\/p>\n Data partitioning is the process of splitting a dataset into training, validation, and test sets. The training set is used to train the model, the validation set is used to evaluate the model’s performance during training, and the test set is used to evaluate the model’s performance after training.<\/p>\n Data visualization is the process of representing data in a graphical or pictorial format. This can be done to make data easier to understand, to identify patterns or trends, or to communicate findings to others.<\/p>\n Data augmentation is the process of artificially increasing the size of a dataset by creating new data points. This can be done by adding noise to existing data points, by generating new data points from scratch, or by combining existing data points in new ways.<\/p>\n Feature engineering is the process of transforming raw data into features that are more useful for machine learning algorithms. This can be done by removing irrelevant data, by combining related data points, or by transforming data into a different format.<\/p>\n","protected":false},"excerpt":{"rendered":" [amp_mcq option1=”Data visualization” option2=”Data augmentation” option3=”Feature engineering” option4=”Data partitioning” correct=”option4″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[738],"tags":[],"class_list":["post-53994","post","type-post","status-publish","format-standard","hentry","category-data-science-miscellaneous","no-featured-image-padding"],"yoast_head":"\n