What is sub-sampling?

Sub-sampling refers to the practice of selecting a smaller portion of a larger data set for use in analysis or modeling. This technique is used to reduce the computational requirements of analysis and to eliminate redundant or irrelevant data. The primary goal of sub-sampling is to create a smaller, more manageable set of data that can be analyzed more efficiently without sacrificing the quality of the results. Sub-sampling can be done randomly or purposefully, depending on the goals of the analysis. One of the key challenges of sub-sampling is selecting the right sample size to achieve the desired results while avoiding overfitting or underfitting the model. Other techniques that may be used in sub-sampling include stratified sampling, cluster sampling, and systematic sampling. Overall, sub-sampling is a powerful technique that can help researchers and analysts extract useful insights from large, complex data sets.