The correct answer is A. Data Augmentation.
Data augmentation is a technique that artificially increases the size of a dataset by creating new data points from existing ones. This can be done by applying various transformations to the data, such as cropping, flipping, or rotating. Data augmentation can be used to improve the performance of machine learning models on imbalanced datasets.
Data scaling is a technique that normalizes the data so that it has a consistent range of values. This can be done by subtracting the mean from each data point and then dividing by the standard deviation. Data scaling can be used to improve the performance of machine learning models on datasets with different scales.
Data encoding is a technique that converts the data into a different format. This can be done by using a variety of encoding schemes, such as one-hot encoding or ordinal encoding. Data encoding can be used to improve the performance of machine learning models on datasets with categorical data.
Data imputation is a technique that fills in missing values in the data. This can be done by using a variety of imputation methods, such as mean imputation or median imputation. Data imputation can be used to improve the performance of machine learning models on datasets with missing values.
In conclusion, data augmentation is the most common technique for handling imbalanced data in classification problems. It can be used to improve the performance of machine learning models by artificially increasing the size of the dataset.