Final answer:
The concept described in the provided question is related to Random Forests and the sampling method used by an instructor for data collection would be considered stratified sampling.
Step-by-step explanation:
The scenario described in the question is consistent with a statistical sampling process used in different types of analyses, including decision trees in machine learning. Specifically, the approach of selecting a subset of rows for each tree and a subset of columns to determine the best split refers to the methodology behind Random Forests, which is an ensemble learning technique used for classification and regression tasks. Based on the information provided, when the instructor takes a sample by gathering data on five randomly selected students from each math class, the type of sampling used is stratified sampling. However, when many focal communities are sampled, it may lead to a cluster analysis, which is a technique to create classifications and can involve various approaches such as divisive or agglomerative, hierarchical or reticulate, qualitative or quantitative data. In the context of survey research, choosing every tenth customer to survey represents systematic sampling, and conducting interviews with all employees in a randomly selected few departments falls under cluster sampling.