Partitions (Multilayer Perceptron)

Partition Dataset. This group specifies the method of partitioning the active dataset into training, testing, and holdout samples. The training sample comprises the data records used to train the neural network; some percentage of cases in the dataset must be assigned to the training sample in order to obtain a model. The testing sample is an independent set of data records used to track errors during training in order to prevent overtraining. It is highly recommended that you create a testing sample, and network training will generally be most efficient if the testing sample is smaller than the training sample. The holdout sample is another independent set of data records used to assess the final neural network; the error for the holdout sample gives an "honest" estimate of the predictive ability of the model because the holdout cases were not used to build the model.

  • Randomly assign cases based on relative number of cases. Specify the relative number (ratio) of cases randomly assigned to each sample (training, testing, and holdout). The % column reports the percentage of cases that will be assigned to each sample based on the relative numbers you have specified.

    For example, specifying 7, 3, 0 as the relative numbers for training, testing, and holdout samples corresponds to 70%, 30%, and 0%. Specifying 2, 1, 1 as the relative numbers corresponds to 50%, 25%, and 25%; 1, 1, 1 corresponds to dividing the dataset into equal thirds among training, testing, and holdout.

  • Use partitioning variable to assign cases. Specify a numeric variable that assigns each case in the active dataset to the training, testing, or holdout sample. Cases with a positive value on the variable are assigned to the training sample, cases with a value of 0, to the testing sample, and cases with a negative value, to the holdout sample. Cases with a system-missing value are excluded from the analysis. Any user-missing values for the partition variable are always treated as valid.

Note: Using a partitioning variable will not guarantee identical results in successive runs of the procedure. See "Replicating results" in the main Multilayer Perceptron topic.

How To Set Partitions for Multilayer Perceptron

This feature requires the Neural Networks option.

  1. From the menus choose:

    Analyze > Neural Networks > Multilayer Perceptron...

  2. In the Multilayer Perceptron dialog box, click the Partitions tab.