The following model fitting options are available:Transforms all continuous variables to near normality using either the Johnson Su or Johnson Sb distribution. Transforming the continuous variables helps mitigate the negative effects of outliers or heavily skewed distributions. See the Save Transformed Covariates option in Model Options.
Choose the penalty method. To mitigate the tendency neural networks have to overfit data, the fitting process incorporates a penalty on the likelihood. See Penalty Method.
The penalty is λp(βi), where λ is the penalty parameter, and p( ) is a function of the parameter estimates, called the penalty function. Validation is used to find the optimal value of the penalty parameter.
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