Following are descriptions of options available on the Neural Network Classification dialogs.

Neural Network Classification - Step 1 of 3

Variables In Input Data

The variables included in the data set appear here.

Selected Variables

Variables selected to be included in the output appear here.

Output Variable

The dependent variable or the variable to be classified appears here.

# Classes

Displays the number of classes in the Output Variable.

Specify "Success" class (for Lift Chart)

This option is selected by default. Click the drop-down arrow to select the value to specify a success. This option is enabled when the number of classes for the Output Variable is equal to 2.

Specify initial cutoff probability for success

Enter a value between 0 and 1 to denote the cutoff probability for success. If the calculated probability for success for an observation is greater than or equal to this value, than a success (1) will be predicted for that observation. If the calculated probability for success for an observation is less than this value, then a non-success (0) will be predicted for that observation. The default value is 0.5. This option is only enabled when the # Classes is equal to 2.

Boosting - Neural Network Classification -- Step 2 of 3 Dialog 

Normalize input data

Normalizing the data (subtracting the mean and dividing by the standard deviation) is important to ensure that the distance measure accords equal weight to each variable -- without normalization, the variable with the largest scale would dominate the measure. This option is selected by default.

Number of weak learners

This option controls the number of weak classification models that are created. The ensemble method stops when the number or classification models created reaches the value set for the Number of weak learners. The algorithm then computes the weighted sum of votes for each class, and assign the winning classification to each record. The default setting is 50.

Boosting algorithm

There are three algorithms available: M1 (Freund), M1 (Breiman), and SAMME. The difference in the algorithms is the way in which the weights assigned to each observation or record are updated. 

In AdaBoost.M1 (Freund), the constant is calculated as:

αb= ln((1-eb)/eb)

In AdaBoost.M1 (Breiman), the constant is calculated as:

αb= 1/2ln((1-eb)/eb)

In SAMME, the constant is calculated as:

αb= 1/2ln((1-eb)/eb + ln(k-1) where k is the number of classes

(When the number of categories is equal to 2, SAMME behaves the same as AdaBoost Breiman.)

Neuron Weight Initialization Seed

If an integer value appears for Neuron weight initialization seed, XLMiner uses this value to set the neuron weight random number seed. Setting the random number seed to a non-zero value, ensures that the same sequence of random numbers is used each time the neuron weights are calculated (default is 12345). If left blank, the random number generator is initialized from the system clock, so the sequence of random numbers is different in each calculation. If you need the results from successive runs of the algorithm to another to be strictly comparable, enter a value for Set seed (either positive or negative up to nine digits).

# Hidden Layers (max 4)

When Manual is selected, this option is enabled. XLMiner supports up to four hidden layers.

# Nodes Per Layer

When Manual is selected, this option is enabled. Enter the number of nodes per layer here.

# Epochs

An epoch is one sweep through all records in the training set. The default setting is 30.

Gradient Descent Step Size

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 (steady) learning, a high value produces rapid (erratic) learning. Values for the step size typically range from 0.1 to 0.9. The default setting is 0.1.

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 default setting is 0.6.

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 from 0 to 1. The default setting is 0.01.

Weight Decay

To prevent over-fitting of the network on the training data, set a weight decay to penalize the weight in each iteration. Each calculated weight will be multiplied by (1-decay). The default setting is 0.

Hidden Layer Activation Function

Nodes in the hidden layer receive input from the input layer. The output of the hidden nodes is a weighted sum of the input values. This weighted sum is computed with weights that are initially set at random values. As the network learns, these weights are adjusted. This weighted sum is used to compute the hidden node's output using a transfer function. Select Standard (default setting) to use a logistic function for the transfer function with a range of 0 and 1. This function has a squashing effect on very small or very large values, but is almost linear in the range where the value of the function is between 0.1 and 0.9. Select Symmetric to use the tanh (tangential) function for the transfer function, the range being -1 to 1. If more than one hidden layer exists, this function is used for all layers. The default selection is Standard.

Output Layer Activation Function

As in the hidden layer output calculation, the output layer is also computed using the same transfer function as described for Hidden Layer Activation Function. Select Standard (default setting) to use a logistic function for the transfer function with a range of 0 and 1. Select Symmetric to use the tanh (tangential) function for the transfer function, the range being -1 to 1.

In neural networks, the Softmax function is often implemented at the final layer of a classification neural network to impose the constraints that the posterior probabilities for the output variable must be >= 0 and <= 1 and sum to 1. Select Softmax to utilize this function. The default selection is Standard.

Partition Options

XLMiner V2015 provides the ability to partition a data set from within a classification or prediction method by selecting Partitioning Options on the Step 2 of 3 dialog. If this option is selected, XLMiner partitions your data set immediately before running the prediction method. If partitioning has already occurred on the data set, this option is disabled. For more information on partitioning, see the Data Mining Partition section.   

Bagging - Neural Network Classification - Step 2 of 3 Dialog

Number of weak learners

This option controls the number of weak classification models that are created. The ensemble method stops when the number or classification models created reaches the value set for the Number of weak learners. The algorithm then computes the weighted sum of votes for each class and assign the winning classification to each record. The default setting is 50.

Neuron Weight Initialization Seed

If an integer value appears for Neuron weight initialization seed, XLMiner uses this value to set the neuron weight random number seed. Setting the random number seed to a non-zero value, ensures that the same sequence of random numbers is used each time the neuron weight are calculated (default is 12345). If left blank, the random number generator is initialized from the system clock, so the sequence of random numbers will be different in each calculation. If you need the results from successive runs of the algorithm to another to be strictly comparable, enter a value for Set seed (positive or negative integers up to 9 digits).

Bootstrapping Random Seed

If an integer value appears for Bootstrapping Random seed, XLMiner uses this value to set the bootstrapping random number seed. Setting the random number seed to a non-zero value, ensures that the same sequence of random numbers is used each time the data set is chosen for the classifer (default is 12345). If left blank, the random number generator is initialized from the system clock, so the sequence of random numbers is different in each calculation. If you need the results from successive runs of the algorithm to another to be strictly comparable, enter a value for Set seed (positive or negative integers up to nine digits).

The Step 3 of 3 dialog contains the same options for each of the four variations: boosting, bagging, automatic, and manual.

Neural Network Classification (Manual Arch.) Step 3 of 3 

Score Training Data

Select these options to show an assessment of the performance of the algorithm in classifying the training data. The report is displayed according to your specifications: Detailed, Summary, and Lift Charts. Lift Charts are disabled when the number of classes is greater than two.

Score Validation Data

These options are enabled when a Validation Set is present. Select to show an assessment of the performance of the algorithm in classifying the Validation Set. The report is displayed according to your specifications: Detailed, Summary, and Lift Charts. Lift Charts are disabled when the number of classes is greater than two.

Score Test Data

These options are enabled when a test set is present. Select these options to show an assessment of the performance of the algorithm in classifying the test data. The report is displayed according to your specifications: Detailed, Summary, and Lift Charts. Lift Charts are disabled when the number of classes is greater than two.

Score New Data

See the Scoring New Data section for more details on the Score New Data options on the Step 3 of 3 dialog.