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Open the file Wine.xls in Microsoft Excel.
This file contains 13 quantitative variables measuring the chemical attributes
of wine samples from 3 different wineries (the Type variable). The
objective is to assign a wine classification to each record..
In XLMiner™, click on Partition
data --> Standard Partition.
In the ensuing dialog box, select all variables in the Variables box and move
them to the "Variables in the partitioned data" box. Select
Specify percentages and enter 80% for the training set and 20% for
the validation set. Select OK.
In XLMiner™, select Classification, then Neural
Network (Multilayer feedforward) option. In the Neural network
dialog box, move the variable "Type" to the "Output variable"
box, and move the remaining variables to the "Input variables"
box.
A more detailed explanation of the above dialog box follows:
Variables: This box lists
all the variables present in the dataset. If the "First row contains
headers" box is checked, the header row above the data is used to
identify variable names.
Variables in input data:
Select one or more variables as independent variables from the Variables box
by clicking on the corresponding selection button. These variables constitute
the predictor variables.
Weight variable: Use this option
if you have data where there are multiple cases (objects) sharing the same
variable values, and the weight variable denotes the number of cases with
those values.
Output Variable: Select one variable as
the dependent variable from the Variables box by clicking on the corresponding
selection button. This is the variable being classified.
Click Next, and the following dialog
box appears. Here you specify the architecture for the neural network.
The second dialog box contains options to define
the network architecture. For this sample, accept the default values.
Details on these choices are explained below the dialog box.
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 will dominate the measure. Check this box.
Number
of hidden layers: Up to four hidden layers can be specified; see the
overview section for more detail on layers in a neural network (input, hidden
and output). Let us specify the number to be 1.
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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.
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).
Cost
Function : XLminer™ provides four options for cost functions --
squared mirror, cross entropy, Maximum likelihood and perceptron
convergence. The user can select the appropriate one.
Hidden
layer sigmoid : The output of every hidden node passes through a
sigmoid function. Standard sigmoid function is logistic, the range is
between 0 and 1. Symmetric sigmoid function is tanh function, the range
being -1 to 1.
Output
layer sigmoid : Standard
sigmoid function is logistic, the range is between 0 and 1. Symmetric
sigmoid function is tanh function, the range being -1 to 1.
Check
the options as shown above. Click Next.
The next dialog box
contains options for scoring data. For this example, check the options
shown below. Click on Finish.
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.
Outputs of the neural network
procedure are displayed in a separate sheet. You can use the Output Navigator to view
various sections of the output.
