CALL td_analyze (
'decisiontree',
'required_parameter_list [ optional_parameter; [...] ]'
);
- required_parameter_list
database = input_database_name;
tablename = input_table_name;
columns = { all | column_name [,...] };
dependent = column_name;
- optional_parameter
{ algorithm = gainratio |
binning = { true | false } |
columnstoexclude = column_name [,...] |
max_depth = max_depth |
min_records = min_records |
operatordatabase = operator_database_name |
outputdatabase = output_database_name |
outputtablename = output_table_name |
override = { true | false } |
overwrite = { true | false } |
pruning = { gainratio | none }
}
Syntax Elements
- database
- The database containing the input table.
- tablename
- The input table from which to build a predictive model.
- columns
- The columns to analyze.
-
| keyword |
Description |
|---|
| all |
All columns. |
| allnumeric |
All numeric columns. |
| allcharacter |
All character columns. |
- dependent
- The name of an input table column whose values are to be predicted.
- algorithm
- [Optional] The algorithm the decision tree uses during building.
- binning
- [Optional] Whether to separate continuous data into 100 bins.
- If the variable has fewer than 100 distinct values, the function ignores this option.
- Default: false
- columnstoexclude
- [Optional] The columns to exclude when columns specifies a keyword.
- Any groupby columns are automatically excluded.
- max_depth
- [Optional] The maximum number of levels the tree can grow.
- Default: 100
- min_records
- [Optional] How far the decision tree can split. Unless a node is pure (meaning it has only observations with the same dependent value), it splits if each branch that can come off this node contains at least this many observations.
- Default: minimum of two cases for each branch
- operatordatabase
- [Optional] The database where the table operators that td_analyze calls reside.
- Default behavior: The function searches the standard search path for table operators.
- outputdatabase
- [Optional] The name of the database containing the output table.
- outputtablename
- [Optional] The name of the output table representing the decision tree model.
- override
- [Optional] An error occurs if the dependent variable has more than 100 distinct values. You can override this limitation and build a decision tree by adding the override parameter and setting it to true. Note that if you have too many distinct values, a Segmentation Violation may occur.
- Default: false
- overwrite
- [Optional] Whether to drop the output tables before creating new ones.
- Default: true
- pruning
- [Optional] The style of pruning to use after the tree is built.
- Default: gainratio