What concept describes the use of a smaller set of data to understand a larger dataset in machine learning?

The concept that best describes the use of a smaller set of data to understand a larger dataset in machine learning is data sampling. Data sampling involves selecting a subset of data from a larger dataset in order to analyze it and draw inferences about the whole dataset. This technique is particularly useful when dealing with large datasets where it may be impractical or time-consuming to analyze all available data. Sampling allows for quicker analysis, helps in identifying trends, and can significantly reduce computational loads while still providing insights that are representative of the larger data.

Data sampling is essential in scenarios such as model training, performance evaluation, and exploratory data analysis. For instance, when developing machine learning models, sampling helps speed up experiments and testing without losing the representativeness of the data.

Data mining, on the other hand, refers to the process of discovering patterns and knowledge from large amounts of data, but it does not specifically imply analysis of a smaller set of data. Data augmentation involves creating new data points from existing data through techniques such as rotations, translations, or distortions, which is often used to improve model performance but is not about understanding a larger dataset through a smaller one. Data cleansing focuses on correcting or removing errors in the data to enhance its quality but is not directly related