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
|
Parameter |
Description |
|---|---|
| Category | Model |
| MADlib version | 1.8+ |
| Data source type | DB |
| Send 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.
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.
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 | Notes or helpful information about this operator's parameter settings. When you enter content in the Notes field, a yellow asterisk appears 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
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
Example
