Tree to Rules
Synopsis
This operator is a meta learner. It uses an inner tree learner for creating a rule model.
Description
The Tree to Rules operator determines a set of rules from the given decision tree model. This operator is a nested operator i.e. it has a subprocess. The subprocess must have a tree learner i.e. an operator that expects an ExampleSet and generates a tree model. This operator builds a rule model using the tree learner provided in its subprocess. You need to have basic understanding of subprocesses in order to apply this operator. Please study the documentation of the
Decision tree is a predictive model which maps observations about an item to conclusions about the item's target value. In these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making.
Input
training set
This input port expects an ExampleSet. It is the output of the Retrieve operator in the attached Example Process. The output of other operators can also be used as input.
Output
model
The rule model is delivered from this output port which can now be applied on unseen data sets for prediction of thelabelattribute.
example set
The ExampleSet that was given as input is passed without changing to the output through this port. This is usually used to reuse the same ExampleSet in further operators or to view the ExampleSet in the Results Workspace.