Attribute sampling is a data reduction technique that involves selecting only the most relevant attributes from a dataset rather than using all available variables.
When preparing data for a machine learning model, including every available attribute isn't always better. Some variables contribute meaningfully to predictions while others add complexity without improving accuracy. Attribute sampling is the process of identifying and keeping only those that matter for the task at hand.
For a model predicting which customers are likely to make large purchases, age, location, and browsing behavior may be strong predictors, whereas a customer ID adds nothing useful.
The selection isn't purely mechanical. Domain expertise plays a significant role, since knowing which variables are likely to be meaningful requires an understanding of the problem itself, not just the data.