What technique allows you to create repeatable samples of your data?

Utilizing the last digits of a hash function is a technique that can create repeatable samples of data. When you hash your data using a consistent hashing algorithm, you can produce the same output for the same input every time. By selecting samples based on the last digits of these hash values, you ensure uniformity and repeatability in your sampling process. This method ties the randomness of sampling to a deterministic process, meaning that as long as the data and hashing function remain unchanged, the resulting samples will remain consistent across different executions.

Other techniques mentioned, like random sampling, stratified sampling, or K-Fold cross-validation, do not guarantee repeatability without specific measures taken to maintain the same conditions across sample generations. For instance, random sampling can lead to different selections of data with each execution unless a fixed random seed is used. Stratified sampling, while methodical, still relies on sets of data that could vary if the base data changes. K-Fold cross-validation, on the other hand, is primarily used for model assessment and not designed as a means to create repeatable data samples. Thus, utilizing a hash function effectively ensures that the same samples can be produced reliably when needed.