requests that specified proportions of the observations in the predictor data set be randomly assigned to training and validation
roles. You specify the proportions for testing and validation by using the VALIDATE= suboption. The SEED= suboption sets the
seed. Because fraction is a per-observation probability, setting fraction too low can result in an empty or nearly empty validation set.
The default seed is based on the date and time.
Using the FRACTION option can cause different numbers of observations to be selected for the validation set because this option
specifies a per-observation probability. Different partitions can be observed when the number of nodes or threads changes
or when PROC HPSPLIT runs in alongside-the-database mode.
The following PARTITION
statement shows how to use a probability of choosing a particular observation for the validation set:
partition fraction(validate=0.1 seed=1234);
In this example, any particular observation has a probability of 10% of being selected for the validation set. All nonselected
records are in the training set. The SEED= suboption specifies the seed that is used for the random number generator.
names the variable in the predictor data set whose values are used to assign roles to each observation. The formatted values of this variable, which are used to assign observations roles, are specified in the TRAIN= and VALIDATE= suboptions.
In the following example, the ROLEVAR= option specifies _PARTIND_ as the variable in the predictor data set that is used to select the data set:
partition rolevar=_partind_(TRAIN='1' VALIDATE='0');
The TRAIN= and VALIDATE= suboptions provide the values that indicate whether an observation is in the training or validation
set, respectively. Observations in which the variable is missing or a value corresponds to neither argument are ignored. Formatting
and normalization are performed before comparison, so you should specify numeric variable values as formatted values, as in
the preceding example.
