Description of the Neural Launch Window 

Y, Response	Choose the response variable. When multiple responses are specified, the models for the responses share all parameters in the hidden layers (those parameters not connected to the responses).
X, Factor	Choose the input variables.
Freq	Choose a frequency variable.
Validation	Choose a validation column. For more information, see Validation Method. If you click the Validation button with no columns selected in the Select Columns list, you can add a validation column to your data table. For more information about the Make Validation Column utility, see Basic Analysis.
By	Choose a variable to create separate models for each level of the variable.
Missing Value Coding	Check this box to enable informative coding of missing values. This coding allows estimation of a predictive model despite the presence of missing values. It is useful in situations where missing data are informative. If this option is not checked, rows with missing values are ignored. For a continuous variable, missing values are replaced by the mean of the variable. Also, a missing value indicator, named <colname> Is Missing, is created and included in the model. If a variable is transformed using the Transform Covariates fitting option on the Model Launch window, missing values are replaced by the mean of the transformed variable. For a categorical variable, missing values are treated as a separate level of that variable.

Description of the Model Launch Dialog 

Validation Method	Select the method that you want to use for model validation. For details, see Validation Method.
Hidden Layer Structure or Hidden Nodes	Note: The standard edition of JMP uses only the TanH activation function, and can fit only neural networks with one hidden layer. Specify the number of hidden nodes of each type in each layer. For details, see Hidden Layer Structure.
Boosting	Specify options for gradient boosting. For details, see Boosting.
Fitting Options	Specify options for variable transformation and model fitting. For details, see Fitting Options.
Go	Fits the neural network model and shows the model reports.

Validation Methods 

Excluded Rows	Uses row states to subset the data. Rows that are unexcluded are used as the training set, and excluded rows are used as the validation set. For more information about using row states and how to exclude rows, see Using JMP.
Holdback	Randomly divides the original data into the training and validation sets. You can specify the proportion of the original data to use as the validation set (holdback).
KFold	Divides the original data into K subsets. In turn, each of the K sets is used to validate the model fit on the rest of the data, fitting a total of K models. The model giving the best validation statistic is chosen as the final model. This method is best for small data sets, because it makes efficient use of limited amounts of data.
Validation Column	Uses the column’s values to divide the data into parts. The column is assigned using the Validation role on the Neural launch window. See The Neural Launch Window. The column’s values determine how the data is split, and what method is used for validation: • If the column has three unique values, then: ‒ the smallest value is used for the Training set. ‒ the middle value is used for the Validation set. ‒ the largest value is used for the Test set. • If the column has two unique values, then only Training and Validation sets are used. • If the column has more than three unique values, then KFold validation is performed.

Activation Functions 

TanH	The hyperbolic tangent function is a sigmoid function. TanH transforms values to be between -1 and 1, and is the centered and scaled version of the logistic function. The hyperbolic tangent function is: where x is a linear combination of the X variables.
Linear	The identity function. The linear combination of the X variables is not transformed. The Linear activation function is most often used in conjunction with one of the non-linear activation functions. In this case, the Linear activation function is placed in the second layer, and the non-linear activation functions are placed in the first layer. This is useful if you want to first reduce the dimensionality of the X variables, and then have a nonlinear model for the Y variables. For a continuous Y variable, if only Linear activation functions are used, the model for the Y variable reduces to a linear combination of the X variables. For a nominal or ordinal Y variable, the model reduces to a logistic regression.
Gaussian	The Gaussian function. Use this option for radial basis function behavior, or when the response surface is Gaussian (normal) in shape. The Gaussian function is: where x is a linear combination of the X variables.

Fitting Options 

Transform Covariates	Transforms all continuous variables to near normality using either the Johnson Su or Johnson Sb distribution. Transforming the continuous variables helps to mitigate the negative effects of outliers or heavily skewed distributions. See the Save Transformed Covariates option in 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.

Descriptions of Penalty Methods 

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.