[amp_mcq option1=”Visualizing data” option2=”Training a model with more features” option3=”Removing noisy data” option4=”Techniques for increasing the diversity of training data by applying transformations” correct=”option4″]<\/p>\n
The correct answer is D. Techniques for increasing the diversity of training data by applying transformations.<\/p>\n
Data augmentation is a technique used in machine learning to artificially increase the size of a training dataset. This can be done by applying various transformations to the existing data, such as cropping, flipping, or rotating images. The goal of data augmentation is to make the model more robust to noise and variations in the data. This can lead to improved performance on unseen data.<\/p>\n
Option A is incorrect because data visualization is a technique used to explore and understand data. It does not involve increasing the size of the dataset.<\/p>\n
Option B is incorrect because training a model with more features is a technique used to improve the performance of a model. It does not involve increasing the size of the dataset.<\/p>\n
Option C is incorrect because removing noisy data is a technique used to improve the quality of the dataset. It does not involve increasing the size of the dataset.<\/p>\n","protected":false},"excerpt":{"rendered":"
[amp_mcq option1=”Visualizing data” option2=”Training a model with more features” option3=”Removing noisy data” option4=”Techniques for increasing the diversity of training data by applying transformations” 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-53411","post","type-post","status-publish","format-standard","hentry","category-data-science-miscellaneous","no-featured-image-padding"],"yoast_head":"\n