Stratified Sampling

Description

To avoid the drawbacks of simple random sampling, you can divide the population to several groups according to your requirements, for example the labels, and sample from each group separately. Each group is called a stratum and this method is called stratified sampling.

For example, to sample 1% of a population that has classes A and B, you can divide the population to two groups and sample 1% from the two groups, respectively. In this way, no matter how rare A or B is, the sampled subsets are ensured to contain both of the two classes.

However, a drawback of stratified sampling is that the population is not always divisible. For example, in a multi-label learning task in which each sample has multiple labels, it is challenging to divide the population according to different labels.