Description: Subsampling is a technique used to reduce the size of a dataset by selecting a representative subset of the original data. This process is fundamental in the fields of machine learning and data science, as it allows for more efficient handling of large volumes of information. By selecting a subset, the goal is to maintain the diversity and representativeness of the original dataset, which helps to avoid overfitting and improve model generalization. Subsampling can be random, where data is chosen arbitrarily, or it can be targeted, where specific data is selected based on certain characteristics or criteria. This technique is particularly useful in situations where data is imbalanced, meaning some classes are overrepresented compared to others. In such cases, subsampling can help balance the classes and improve model performance. Additionally, subsampling is used in hyperparameter optimization, where the aim is to reduce training time by working with a smaller dataset without sacrificing the quality of the final model.
