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Which ONE of the following activities is MOST relevant when addressing the scenario where you have more than the required amount of data available for the training? SELECT ONE OPTION
Correct Answer: B
* A. Feature selection * Feature selection is the process of selecting the most relevant features from the data. While important, it is not directly about handling excess data. * B. Data sampling * Data sampling involves selecting a representative subset of the data for training. When there is more data than needed, sampling can be used to create a manageable dataset that maintains the statistical properties of the full dataset. * C. Data labeling * Data labeling involves annotating data for supervised learning. It is necessary for training models but does not address the issue of having excess data. * D. Data augmentation * Data augmentation is used to increase the size of the training dataset by creating modified versions of existing data. It is useful when there is insufficient data, not when there is excess data. Therefore, the correct answer isBbecause data sampling is the most relevant activity when dealing with an excess amount of data for training.
