PARTITION Statement
The PARTITION statement specifies how observations in the input data set are logically partitioned into disjoint subsets for model training, validation, and testing. For more information, see the section Using Validation and Test Data in Chapter 3: Shared Concepts. 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 randomly assigning observations to each role.
You must specify exactly one of the following partition-options:
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FRACTION(<TEST=fraction> <VALIDATE=fraction> <SEED=number>)
randomly assigns specified proportions of the observations in the input data table to the roles. You specify the proportions for testing and validation by using the TEST= and VALIDATE= suboptions. If you specify both the TEST= and VALIDATE= suboptions, then the sum of the specified fractions must be less than 1 and the remaining fraction of the observations are assigned to the training role. The SEED= option specifies an integer that is used to start the pseudorandom number generator for random partitioning of data for training, testing, and validation. 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.
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ROLE=variable (<TEST='value'> <TRAIN='value'> <VALIDATE='value'>)
ROLEVAR=variable (<TEST='value'> <TRAIN='value'> <VALIDATE='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=, TRAIN=, and VALIDATE= suboptions specify the formatted values of this variable that are used to assign observation roles. If you do not specify the TRAIN= suboption, then all observations whose role is not determined by the TEST= or VALIDATE= suboption are assigned to the training role.