Decision Tree Regression - CART

Generates a decision tree that predicts the value of a numeric column based on several independent columns.

Information at a Glance

Category Model
MADlib version 1.8+
Data source type DB
Sends output to other operators Yes
Data processing tool MADlib

This operator uses the MADlib built-in function, tree_train(). The generated tree is a binary tree, with each node representing either a branching condition or a predicted value. The output of the operator can be sent to a predictor or confusion matrix. MADlib 1.8 or higher must be installed on the database.

Important: This operator does not work with MADlib 1.7.1 or lower, due to a change in the way MADlib handles column names. If you have an older version of MADlib, consider using Decision Tree - MADlib instead.

For more information about working with decision trees, see Classification Modeling with Decision Tree.

Input

The input table must have a single, numeric (floating point) column to predict, and one or more independent columns to serve as input.

Bad or Missing Values
Any rows in the source table that contains NULL values for the predicted or independent columns are ignored.

Restrictions

This operator works only on databases with MADlib 1.8+ installed. Source data tables must have a numeric ID column that uniquely identifies each row in the source table. The prediction column must be numeric, and all predictions are double-precision values.

Configuration

Parameter Description
Notes Any notes or helpful information about this operator's parameter settings. When you enter content in the Notes field, a yellow asterisk is displayed on the operator.
MADlib Schema The name of schema where MADlib is installed. By default, this is madlib.
ID column All source tables must have a numeric ID column to uniquely identify each row.
Dependent Variable Name of numeric column to predict. This must be a floating-point column.
Feature List Selection of one or more columns to use as independent variables to predict the dependent variable.
Model Output Schema The name of the schema to use for MADlib-generated output tables.
Model Output Table The name of the MADlib-generated output table. This table is generated by the tree trainer. An additional table with the same name and the suffix _summary is also generated.
Drop Model Output Tables if They Exist? If yes, generated output tables from a previous run are dropped first. If no, and if the tables already exist when the tree trainer is run, an error occurs.
Split Criterion The algorithm for calculating branch nodes during tree generation. For regression tables, this algorithm is always mse.
Maximum Tree Depth The generated tree does not exceed this depth. If not specified, the default is 10.
Minimum Observations Before Splitting If not specified, the default is 20.
Minimum Observations in Terminal Nodes If not specified, the default is the minimum observations before splitting, divided by 3.
Number of Bins for Split Boundaries If not specified, the default is 100.

Outputs

Visual Output

This operator produces the following tabs.

  • Decision Tree Text - Contains a text representation of the generated decision tree. Each branch node contains a number of rows and a prediction. Branch nodes also contain a branching condition.
  • Decision Tree Graph - Contains a tree graph. Branches reflect split conditions and associated predictions.



Additional Notes

Output to Succeeding Operators
Connect this operator to the following succeeding operators.
  • Prediction
  • Confusion Matrix

Example



Related reference