Sample (Stratified) (AI Studio Core)

Description

The stratified sampling builds random subsets and ensures that the class distribution in the subsets is the same as in the whole ExampleSet. For example in the case of a binominal classification, Stratified sampling builds random subsets such that each subset contains roughly the same proportions of the two values of class labels.

When there are different classes in an ExampleSet, it is sometimes advantageous to sample each class independently. Stratification is the process of dividing examples of the ExampleSet into homogeneous subgroups (i.e. classes) before sampling. The subgroups should be mutually exclusive i.e. every examples in the ExampleSet must be assigned to only one subgroup (or class). The subgroups should also be collectively exhaustive i.e. no example can be excluded. Then random sampling is applied within each subgroup. This often improves the representativeness of the sample by reducing the sampling error.

A real-world example of using stratified sampling would be for a political survey. If the respondents needed to reflect the diversity of the population, the researcher would specifically seek to include participants of various minority groups such as race or religion, based on their proportionality to the total population as mentioned above. A stratified survey could thus claim to be more representative of the population than a survey of simple random sampling or systematic sampling.

In contrast to the simple sampling operator (the Sample operator), this operator performs a stratified sampling of the data sets with nominal label attributes, i.e. the class distributions remains (almost) the same after sampling. Hence, this operator cannot be applied on data sets without a label or with a numerical label. In these cases a simple sampling without stratification should be performed through the Sample operator.

This operator is similar to the Filter Examples operator in principle that it takes an ExampleSet as input and delivers a subset of the ExampleSet as output. The difference is this that the Filter Examples operator filters examples on the basis of specified conditions. But the Sample operator focuses on the number of examples and class distribution in the resultant sample. Moreover, the samples are generated randomly. The number of examples in the sample can be specified on absolute and relative basis depending on the setting of the sample parameter.