SAP HANA Predictive Analysis Library (PAL)

PAL_IQR_RESULTS_TBL:
3.6.5 Partition
The algorithm partitions an input dataset randomly into three disjoints subsets called training, testing, and
validation set. The proportion of each subset is defined as a parameter. Let us remark that the union of these
three subsets might not be the complete initial dataset.
Two different partitions can be obtained:
1. Random Partition, which randomly divides all the data.
2. Stratified Partition, which divides each subpopulation randomly.
In the second case, the dataset needs to have at least one categorical attribute (for example, of type varchar).
The initial dataset will first be subdivided according to the different categorical values of this attribute. Each
mutually exclusive subset will then be randomly split to obtain the training, testing, and validation subsets.
This ensures that all "categorical values" or "strata" will be present in the sampled subset.
Prerequisites
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There is no missing or null data in the column used for stratification.
The column used for stratification must be categorical (integer, varchar, or nvarchar).
448 P U B L I C
SAP HANA Predictive Analysis Library (PAL)
PAL Functions