Discriminant Analysis - Data Mining for the Masses - page 113

7) The tra port on the LDA (or Linear Discriminant Analysis) operator indicates that this tool
does expect to receive input from a training data set like the one we've provided, but
despite this, we still have received two errors, as indicated by the black arrow at the bottom
of the Figure 7-6 image. The first error is because of our Prime_Sport attribute. It is data
typed as polynominal, and LDA likes attributes that are numeric. This is OK, because the
predictor attribute can have a polynominal data type, and the Prime_Sport attribute is the
one we want to predict, so this error will be resolved shortly. This is because it is related to
the second error, which tells us that the LDA operator wants one of our attributes to be
designated as a 'label'. In RapidMiner, the label is the attribute that you want to predict.
At the time that we imported our data set, we could have designated the Prime_Sport
attribute as a label, rather than as a normal attribute, but it is very simple to change an
attribute's role right in your stream. Using the search field in the Operators tab, search for
an operator called Set Role. Add this to your stream and then in the parameters area on
the right side of the window, select Prime_Sport in the name field, and in target role, select
label. We still have a warning (which does not prevent us from continuing), but you will
see the errors have now disappeared at the bottom of the RapidMiner window (Figure 7-7).
Figure 7-7. Setting an attribute's role in RapidMiner.
With our inconsistent data removed and our errors resolved, we are now prepared to move on
to…