Decision Tree Algorithm

The Decision Tree algorithm provides a fast, scalable non-parametric means of extracting predictive information from a database with respect to a user-supplied target. Decision Trees extract predictive information in the form of human-understandable rules. The rules are in the form "IF predictive information THEN target", as in "IF income is greater than $70,000 and household size is greater than 3 THEN the probability of Churn is 0.075."

Decision tree rules provide model transparency so that a business user, marketer, or business analyst can understand the basis of the model's predictions. In addition to transparency, Decision Trees provide speed and scalability. The build algorithm scales linearly with the number of predictor attributes. Scoring is very fast. Both build and apply can be parallelized.

Decision Tree algorithm builds predictive models for binary and multi-class targets on large volumes of data and produce accurate and interpretable models with relatively little user intervention required. The Decision Tree algorithm handles data in the typical data table formats, has reasonable defaults for splitting and termination criteria, performs automatic pruning, and performs automatic handling of missing values.

You can specify costs and priors.