About the Tasks That You Will Perform

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:
  1. 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.
  2. 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.
  3. 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.