The PARTITION statement specifies how observations in the input data set are logically partitioned into disjoint subsets for
model training, validation, and testing. Either you can 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 you can specify proportions to use for random assignment
of observations for each role.
You can specify either of the following mutually exclusive partition-options:
-
FRACTION(<TEST=fraction> <VALIDATE=fraction>)
-
randomly assigns the specified proportions of observations in the input data set to testing, validation, and training 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.
-
ROLEVAR | ROLE=variable(<TEST=’value’> <TRAIN=’value’> <VALIDATE=’value’>)
-
names the variable in the input data set whose values are used to assign roles to each observation. Use the TEST=, TRAIN=,
and VALIDATE= suboptions to specify the formatted values of this variable that are used to assign observation roles are specified
in the TEST=, TRAIN=, and VALIDATE= suboptions. If you do not specify the TRAIN= suboption, then all observations whose roles
are not determined by the TEST= and VALIDATE= suboptions are assigned to training.
To create an output data set variable that indicates the role assignment for either partition-option, specify the ROLE
=variable option in the OUTPUT
statement.
