Properties that can be configured for the R-NNet Neural Network algorithm.
| Property | Description |
|---|---|
| Output Mode | Select the mode in which you want to use the output of this
algorithm. Possible values:
|
| 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:
|
| 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:
|
| 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. |