Data augmentation in data analysis are techniques used to increase the amount of data by adding slightly modified copies of already existing data or newly created synthetic data from existing data. It acts as a regularizer and helps reduce overfitting when training a machine learning model.

What is data augmentation technique?

Data augmentation techniques generate different versions of a real dataset artificially to increase its size. Computer vision and natural language processing (NLP) models use data augmentation strategy to handle with data scarcity and insufficient data diversity.

What is data augmentation and why is it needed?

Data augmentation is a technique to artificially create new training data from existing training data. ... It helps us to increase the size of the dataset and introduce variability in the dataset, without actually collecting new data. The neural network treats these images as distinct images anyway.