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Answer: AutoML supports data augmentation by using techniques such as random cropping, flipping, and rotation for image data, and synthetic data generation for tabular data to increase the size and diversity of the dataset.
Data augmentation is a technique used to increase the size and diversity of a dataset, especially when dealing with limited data. AutoML can automate this process by using techniques such as random cropping, flipping, and rotation for image data, which create new variations of the images; and synthetic data generation for tabular data, which creates new data points based on the patterns and relationships in the original dataset. These techniques help to improve the model's performance by providing more diverse and representative data for training. Option C correctly describes the techniques used by AutoML for data augmentation and their significance in improving model performance.
Author: LeetQuiz Editorial Team
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In the context of AutoML, explain the role of data augmentation and how AutoML can automate this process. Provide a detailed explanation of the techniques used by AutoML for data augmentation and their significance in improving model performance, especially in cases of limited data.
A
AutoML does not support data augmentation, as it relies on the original dataset for training the model.
B
AutoML supports data augmentation by manually creating new variations of the existing data, such as adding noise or transforming the data.
C
AutoML supports data augmentation by using techniques such as random cropping, flipping, and rotation for image data, and synthetic data generation for tabular data to increase the size and diversity of the dataset.
D
AutoML supports data augmentation by automatically selecting a subset of the original data and applying random transformations to create new variations.
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