The correct answer is: Oversampling.
Oversampling is a technique in machine learning that is used to balance the class distribution of a dataset. This is done by creating additional copies of minority class instances. Oversampling can be used to improve the performance of machine learning models, especially when the minority class is very small.
Regularization is a technique that is used to prevent overfitting in machine learning models. This is done by adding a penalty term to the loss function that discourages the model from becoming too complex. Regularization can be used with any type of machine learning model, but it is most commonly used with neural networks.
Feature extraction is a technique that is used to reduce the dimensionality of a dataset. This is done by identifying and removing features that are not relevant to the task at hand. Feature extraction can be used to improve the performance of machine learning models, especially when the dataset is large.
Undersampling is a technique in machine learning that is used to balance the class distribution of a dataset. This is done by removing majority class instances. Undersampling can be used to improve the performance of machine learning models, especially when the majority class is very large.
In conclusion, the correct answer is: Oversampling. Oversampling is a technique in machine learning that is used to balance the class distribution of a dataset. This is done by creating additional copies of minority class instances. Oversampling can be used to improve the performance of machine learning models, especially when the minority class is very small.