PARTITION Statement
The PARTITION statement specifies how to logically partition observations in the input data table into disjoint subsets for model training and testing. Either you can designate a variable in the input data table and a set of formatted values of that variable to determine the role of each observation, or you can specify proportions to use for random assignment of observations to each role.
You must specify exactly one of the following partition-options:
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FRACTION( <TEST=fraction> <SEED=number> )
requests that specified proportions of the observations in the input data table be randomly assigned training and testing roles. You specify the proportions for testing by using the TEST= suboption; the specified fraction must be less than 1 and the remaining fraction of the observations are assigned to the training role. If you do not specify the TEST= suboption, all observations are assigned to the training role. The SEED= suboption specifies an integer that is used to start the pseudorandom number generator for random partitioning of data for training and testing. If you do not specify SEED=number or if number is less than or equal to 0, the seed is generated by reading the time of day from the computer’s clock.
Because fraction is a per-observation probability (which means that any particular observation has a probability of fraction of being assigned the testing role), using the FRACTION option can cause different numbers of observations to be assigned training and testing roles. You can specify the SEED= suboption to create the same partition data tables for a particular number of compute nodes. However, changing the number of compute nodes changes the initial distribution of data, resulting in different partition data tables.
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ROLEVAR | ROLE=variable (<TEST='value'> <TRAIN='value'>)
names the variable in the input data table whose values are used to assign roles to each observation. This variable cannot also appear as an analysis variable in other statements or options. The TEST= and TRAIN= suboptions specify the formatted values of this variable that are used to assign observation roles. If you specify only the TEST= suboption, then all observations whose role is not determined by the TEST= suboption are assigned to training.
For more information, see the section Test Set Validation. For an illustration, see Example 17.1.