The RAND("Table") function is an efficient way to generate the indicator variable.ĭata Have /* the data to partition */ set Sashelp.Heart /* for example, use Heart data */ run You can change the values of the SAS macro variables to use your own proportions. The specified proportions are 60% training, 30% validation, and 10% testing. The following DATA step creates an indicator variable with values "Train", "Validate", and "Test". When you partition data into various roles, you can choose to add an indicator variable, or you can physically create three separate data sets. Random partition into training, validation, and testing data However, be aware that the procedures might ignore observations that have missing values for the variables in the model. Example include the "SELECT" procedures (GLMSELECT, QUANTSELECT, HPGENSELECT.) and the ADAPTIVEREG procedure. It is worth mentioning that many model-selection routines in SAS enable you to split data by using the PARTITION statement. I also discuss how to split data into only two roles: training and validation. This article uses the SAS DATA step to accomplish the first task and uses PROC SURVEYSELECT to accomplish the second. Specify the number of observations that you want in each role and randomly allocate that many observations. For this method, if you change the random number seed you will usually get a different number of observations each role because the size is a random variable. The number of observations assigned to each role will be a multinomial random variable with expected value N p k, where N is the number of observations and p k ( k = 1, 2, 3) is the probability of assigning an observation to the k_th role. For each observation, randomly assign it to one of the three roles.
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