R-MONMLP Neural Network

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

Syntax Use this algorithm for forecasting, classification, and statistical pattern recognition using R library functions.
Note R does not support PMML storage for MONMLP Neural Network.
R-MONMLP 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 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).