The Sampling function draws rows randomly from the input table.
The function offers two sampling schemes:
- A simple Bernoulli (Binomial) sampling on a row-by-row basis with given sample rates
- Sampling without replacement that selects a given number of rows
Sampling can be either unconditional or conditional. Unconditional sampling applies to all input data and always uses the same random number generator. Conditional sampling applies only to input data that meets specified conditions and uses a different random number generator for each condition.
The Sampling function does not guarantee the exact sizes of samples. If each sample must have an exact number of rows, use the RandomSample (ML Engine) function.