Now that you have verified the input data, it is time to build predictive models.
You perform the following tasks to
model the input data using nonparametric decision trees:
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You enable SAS Enterprise Miner to automatically train
a full decision tree and to automatically prune the tree to an optimal
size. When training the tree, you select split rules at each step
to maximize the split decision logworth. Split decision logworth is
a statistic that measures the effectiveness of a particular split
decision at differentiating values of the target variable. For more
information about logworth, see the SAS Enterprise Miner Help.
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You interactively train a decision tree. At each step,
you select from a list of candidate rules to define the split rule
that you deem to be the best.
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You use a Gradient Boosting node to generate a set
of decision trees that form a single predictive model. Gradient boosting
is a boosting approach that resamples the analysis data set several
times to generate results that form a weighted average of the re-sampled
data set.