Fitting Options
The following model fitting options are available:
Transform Covariates
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 Neural Model Options.
Robust Fit
Trains the model using least absolute deviations instead of least squares. This option is useful if you want to minimize the impact of response outliers. This option is available only for continuous responses.
Penalty Method
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.
Number of Tours
Specify the number of times to restart the fitting process, with each iteration using different random starting points for the parameter estimates. The iteration with the best validation statistic is chosen as the final model.
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.
|
Method |
Penalty Function |
Description |
|---|---|---|
|
Squared |
|
Use this method if you think that most of your X variables are contributing to the predictive ability of the model. |
|
Absolute |
|
Use either of these methods if you have a large number of X variables, and you think that a few of them contribute more than others to the predictive ability of the model. |
|
Weight Decay |
|
|
|
NoPenalty |
none |
Does not use a penalty. You can use this option if you have a large amount of data and you want the fitting process to go quickly. However, this option can lead to models with lower predictive performance than models that use a penalty. |
