A decision tree is a visualization technique that
enables you to classify observations into groups and predict future events
based on the set of decision rules.
This presentation is used for decision tree analysis.
In this technique, a binary decision tree is built by splitting observations
into smaller sub-groups until the stopping criterion is met. The leaf node
indicates classified data. You can enlarge the decision tree by choosing the
zoom-in button.
Note It is not possible to render a decision tree if there are more than 32 categorical values for
a dependent column.
Note The look and feel of the decision tree differs
based on the algorithm vendor. For example, the decision tree for the R-CNR
Tree algorithm is different from the decision tree for the HANA C4.5 algorithm.
Each node in the decision tree represents the
classification of data at that level. You can view node contents by choosing

on each node.