The HPNEURAL Procedure

If you specify a WEIGHT statement, variable identifies a numeric variable in the input data set that contains the weight to be placed on the prediction error (the difference between the output of the network and the target value specified in the input data set) for each observation during training.

If, instead of specifying a variable, you specify the keyword _INVERSE_PRIORS_, the HPNEURAL procedure calculates the weight applied to the prediction error of each nominal target variable as the total number of observations divided by the number of observations whose target class is the same as the current observation (in other words, the inverse of the fraction of the number of times that the target class occurs in the input data set).

If variable is less than or equal to 0 or is missing, the observation is not used for training or for computing validation error. When validation error is computed during training, the weights on the validation observations are used even though weights are not used when scoring.

The WEIGHT statement is optional. If a WEIGHT statement is not included, all observations are assigned a weight of 1.