11/15/2023 0 Comments Sas enterprise miner![]() NOTE: The data set WORK.TRAIN has 3078 observations and 17 variables. The computation is exactly the same, but you can use the OUTPUT statement to direct each observation to one of three output data sets, as follows: ![]() Some practitioners choose to create three separate data sets instead of adding an indicator variable to the existing data. By specifying only two values in the p array, the same program works for partitioning the data into two pieces (training and validation) or three pieces (and testing).Ĭreate random training, validation, and testing data sets The documentation for the RAND("Table") function states that if the sum of the specified probabilities is less than 1, then the quantity 1 – Σ p i is used as the probability of the last event. The observant reader will notice that there are only two elements in the array of probabilities ( p) that is used in the RAND("Table") call. If you change the random number seed, you will get a different assignment of observations to roles and also a different proportion of data in each role. For this random number seed, the proportions are 59.1%, 30.4%, and 10.6%. _ROLE_ = labels /* use _ROLE_ = _k if you prefer numerical categories */ drop _k Ī shown by the output of PROC FREQ, the proportion of observations in each role is approximately the same as the specified proportions. * If propTrain + propValid = 1, then no observation is assigned to testing */ %let propTrain = 0.6 /* proportion of trainging data */ %let propValid = 0.3 /* proportion of validation data */ %let propTest = %sysevalf ( 1 - &propTrain - &propValid ) /* remaining are used for testing */ /* Randomly assign each observation to a role _ROLE_ is indicator variable */ data RandOut Īrray p _temporary_ ( &propTrain, &propValid ) Īrray labels $ _temporary_ ( "Train", "Validate", "Test" ) Ĭall streaminit ( 123 ) /* set random number seed */ /* RAND("table") returns 1, 2, or 3 with specified probabilities */ 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.
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