| Using
Neural Network Prediction in XLMiner™:
In XLMiner™, select Prediction -> Neural Network (Multilayer feedforward). This brings up the following dialog box, where you need to specify the data range to be processed, the input variables and the output variable.
Click Next, and the following dialog box appears. Here you specify the parameters used for the neural network.
Normalize input data: Normalizing the data (subtracting the mean and dividing by the standard deviation) is to ensure that the distance measure accords equal weight to each variable -- without normalization, the variable with the largest scale will dominate the measure. Number of hidden layers: Up to four hidden layers can be specified; see the introduction section for more detail on layers in a neural network (input, hidden and output). # Nodes: Specify the number of nodes in each hidden layer. Selecting the number of hidden layers and the number of nodes is largely a matter of trial and error. # Epochs: An epoch is one sweep through all the records in the training set. Step size for gradient descent: This is the multiplying factor for the error correction during backpropagation; it is roughly equivalent to the learning rate for the neural network. A low value produces slow but steady learning, a high value produces rapid but erratic learning. Values for the step size typically range from 0.1 to 0.9. Weight change momentum: In each new round of error correction, some memory of the prior correction is retained so that an outlier that crops up does not spoil accumulated learning. The momentum value ranges from 0-2. Error tolerance: The error in a particular iteration is backpropagated only if it is greater than the error tolerance. Typically error tolerance is a small value in the range 0 to 1. The default value for error tolerance in XLMiner™ is 0.01. Weight decay: To prevent over-fitting of the network on the training data set a weight decay is used to penalize the weight in each iteration, thus updating it by multiplying the calculated weight by (1-decay). Click Next, and the following dialog appears.
Score training data: Select this option to show an assessment of the performance of the tree in classifying the training data. The report is displayed according to your specifications - Detailed, Summary and Lift charts. Score validation data: Select this option to show an assessment of the performance of the tree in classifying the validation data. The report is displayed according to your specifications - Detailed, Summary and Lift charts. Score Test Data: The options in this group let you apply the model for scoring to the test partition (if one had been created earlier). The option "Score Test Data" is available only if the dataset contains test partition. Select it to apply the model to test data. Score new Data: The options in this group let you apply the model for scoring to an altogether new data. Specify where the new data is located. See the Example of Discriminant Analysis for detailed instructions on this. Score New data in database : See the Example of Discriminant Analysis for detailed instructions on this. Click Finish, and the output will be displayed in a separate sheet. See also |
