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:
-
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.
-
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.
-
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.