Discriminant Analysis - Data Mining for the Masses - page 112

what should have been in this variable, so changing these to the mean seems a bit arbitrary.
Removing this inconsistencies means only removing 11 of our 493 observations, so rather
than risk using bad data, we will simply remove them. To do this, add two Filter Examples
operators in a row to your stream. For each of these, set the condition class to
attribute_value_filter, and for the parameter strings, enter 'Decision_Making>=3' (without
single quotes) for the first one, and 'Decision_Making<=100' for the second one. This
will reduce our training data set down to 482 observations. The set-up described in this
step is shown in Figure 7-5.
Figure 7-5. Filtering out observations with inconsistent data.
6) If you would like, you can run the model to confirm that your number of observations
(examples) has been reduced to 482. Then, in design perspective, use the search field in
the Operators tab to look for 'Discriminant' and locate the operator for Linear
Discriminant Analysis. Add this operator to your stream, as shown in Figure 7-6.
Figure 7-6. Addition of the Linear Discriminant Analysis operator to the model.