The PARTITION statement specifies how observations in the input data set are to be logically partitioned into disjoint subsets
for model training, validation, and testing. For more information, see the section Using Validation and Test Data. You can either designate a variable in the input data set and a set of formatted values of that variable to determine the
role of each observation, or specify proportions to use for random assignment of observations for each role.
You can specify one of the following mutually exclusive partition-options:
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ROLEVAR | ROLE=variable(<TEST=’value’> <TRAIN=’value’> <VALIDATE=’value’>)
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names the variable in the input data set whose values are used to assign roles to each observation. The TEST=, TRAIN=, and
VALIDATE= suboptions specify the formatted values of this variable that are used to assign observations roles. If you do not
specify the TRAIN= suboption, then all observations whose role is not determined by the TEST= or VALIDATE= suboptions are
assigned to training.
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FRACTION(<TEST=fraction> <VALIDATE=fraction> <SEED=number>)
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randomly assigns specified proportions of the observations in the input data set 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 the 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 a seed, or if you specify a number less than or equal to 0, the seed is generated by reading the time of day from the computer’s clock.
