SANN - Data Selection - Subsampling Tab

You can select the Subsampling tab of the SANN - Data selection dialog box to access the options described here.

Option Description
Subsampling method Options in this group box specify which subsampling method you want to use.
Random Generates multiple subsamples of the original data set based upon sampling without replacement.
Bootstrap Generates multiple subsamples of the original data set based upon sampling with replacement.
Size of subsamples (%) Options in this group box specify the percentages of cases in the data set to allocate to the train, test, and validation samples.
Train subsample Specifies the percent of valid cases to use in the training sample. Must be greater than 0 and less than or equal to 100. This option is not available for deployment.
Test subsample Randomly assigns cases to a test sample. Specifies the percentage of cases to use. Zero sample size means no test sample is requested (not recommended).
Validation subsample Randomly assigns cases to a validation sample. Specifies the percentage of cases to use. This option is not available for deployment. Zero sample size means no test sample is requested (not recommended).
Number of subsamples Specifies the number of subsamples you want to create. This is equal to the number of neural networks that will be created, that is, one neural network is created for each subsample. In theory the more subsamples (and, hence, neural networks) you create the better, but from a practical standpoint, you should limit this number depending on your time and computer resources.
Seed for subsampling The positive integer value entered here is used as the seed for a random number generator that produces the random subsamples from the data. Starting from the same seed will yield the same subsample. If you want to create a different data subsample, change the seed value.