Properties that can be configured for the R-MONMLP 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 the input columns with which you want to perform the analysis. |
| Target Variable | Select the target column for which you want to perform the analysis. |
| Hidden Layer1 Neurons | Enter the number of nodes/neurons in the first hidden layer (hidden1). The default value is 5. |
| Predicted Column Name | Enter a name for the newly created column that contains the predicted values. |
| Hidden Layer Transfer Function | Select the activation function to be used for the hidden layer (Th). |
| Output Layer Transfer Function | Select the activation function to be used for the output layer (To). |
| Derivative of Hidden Layer Transfer Function | Select the derivative of the hidden layer activation function (Th.prime). |
| Derivative of Output Layer Transfer Function | Select the derivative of the output layer activation function (To.prime). |
| Hidden Layer2 Neurons | Enter the number of nodes/neurons in the second hidden layer (hidden2). The default value is 0. |
| Maximum Iterations | Enter the maximum number of iterations for the optimization algorithm (iter.max). The default value is 5000. |
| Monotone Columns | Enter column indexes to which you want to apply the monotonicity constraint (monotone). |
| Training Iterations | Enter the number of training iterations after which the cost function calculation stops (iter.stopped). |
| Initial Weights | Enter an initial weight vector (init.weights). |
| Maximum Exceptions | Enter the maximum number of exceptions for the optimization routine (max.exceptions). |
| Scale Dependent Column | To scale dependent columns to zero mean and unit variance prior to fitting, select True (scale.y). |
| Bagging Required | To use bootstrap aggregation, select True (bag). |
| Trials to Avoid Local Minima | Enter the number of repeated trials to avoid local minima (n.trials). |
| No. Ensemble Members | Enter the number of ensemble members to fit (n.ensemble). |