Decision Tree Algorithm

The Decision Tree algorithm is based on conditional probabilities. Unlike Naive Bayes, Decision Trees generate rules. A rule is a conditional statement that can easily be used by humans and easily used within a database to identify a set of records.

The Decision Tree algorithm:

• Creates accurate and interpretable models with relatively little user intervention. The algorithm can be used for both binary and multiclass classification problems.
  The algorithm is fast, both at build time and apply time. The build process for the Decision Tree Algorithm is parallelized. Scoring can be parallelized irrespective of the algorithm.

• Predicts a target value by asking a sequence of questions. At a given stage in the sequence, the question that is asked depends upon the answers to the previous questions. The goal is to ask questions that together uniquely identify specific target values.

Decision Tree scoring is especially fast. The tree structure, created in the model build, is used for a series of simple tests, (typically 2-7). Each test is based on a single predictor. It is a membership test: either IN or NOT IN a list of values (categorical predictor); or LESS THAN or EQUAL TO some value (numeric predictor).

During the model build, the Decision Tree algorithm must repeatedly find the most efficient way to split a set of cases (records) into two child nodes. Oracle Data Mining offers two homogeneity metrics, gini and entropy, for calculating the splits. The default metric is gini.