R-NNet Neural Network

Properties that can be configured for the R-NNet Neural Network algorithm.

Syntax Use this algorithm for forecasting, classification, and statistical pattern recognition using R library functions.
R-NNet Neural Network Properties
Table 1: Algorithm Properties
Property Description
Output Mode Select the mode in which you want to use the output of this algorithm.
Possible values:
  • Trend: Predicts the values for the dependent column and adds an extra column in the output containing the predicted values.
  • Fill: Fills missing values in the target column.
Features Select input columns with which you want to perform the analysis.
Target Variable Select the target column for which you want to perform the analysis.
Missing Values Select the method for handling missing values.
Possible values:
  • Ignore: The algorithm skips the records containing missing values in the independent or dependent columns.
  • Keep: The algorithm retains missing values.
  • Stop: The algorithm stops if a value is missing in the independent column or the dependent column.
Hidden Layer Neurons Enter the number of nodes/neurons in the hidden layer. The default value is 5.
Predicted Column Name Enter a name for the newly created column that contains the predicted values.
Algorithm Type Select the type of analysis you want the algorithm to perform.
Skip Hidden Layer To add skip-layer connections from input to output, select True.
Linear Output To obtain the linear output, select True. If you select the algorithm type as Classification, then this value must be true.
Use Softmax Select True to use "log-linear model" and "maximum conditional likelihood" fittings.

Linout, entropy, softmax, and censored are mutually exclusive.

Use Entropy To use "Maximum Conditional Likelihood" fitting, select True. By default, the algorithm uses the least-squares method.
Possible values:
  • True: Use the "Maximum Conditional Likelihood" fitting
  • False: Use the least-squares method
Use Censored For softmax, a row of (0,1,1) indicates one example each of classes 2 and 3, but for censored it indicates one example each of classes 2 or 3.
Range Enter initial random weights [-rang, rang]. Set this value to 0.5 unless the input is large. If the input is large, choose the rang using the formula: rang * max(|x|) <= 1.
Weight Decay Enter a value used for calculating new weights (weight decay).
Maximum Iterations Enter the maximum number of iterations allowed.
Hessian Matrix Required To return the Hessian measure at the best set of weights, select True.
Maximum Weights

Enter the maximum number of weights allowed in the calculation.

There is no intrinsic limit in the code, but increasing the maximum number of weights may allow fits that are very slow and time-consuming.

Abstol Enter the value that indicates the perfect fit (abstol).
Reltol Algorithm terminates if the optimizer is unable to reduce the fit criterion by a factor: 1 - reltol.
Contrasts Enter the list of contrasts to be used for factors appearing as variables in the model.