| AttributeTable |
Required |
Specifies the name of the table that contains the attributes and values of the data. |
| AttributeNameColumns |
Required |
Specifies the names of attribute table columns that contain the data attributes. |
| AttributeValueColumn |
Required |
Specifies the names of attribute table columns that contain the data values. |
| CategoricalAttributeTable |
Optional |
Specifies the name of the table that contains the names of the categorical attributes. |
| ResponseTable |
Required |
Specifies the name of the table that contains the responses (labels) of the data. |
| OutputTable |
Required |
Specifies the name of the output table in which the function stores the predictive model it generates. |
| IdColumns |
Required |
Specifies the names of the columns in the response and attribute tables that specify the identifier of the instance. |
| ResponseColumn |
Required |
Specifies the name of the response table column that contains the responses (labels) of the data. |
| IterNum |
Optional |
Specifies the number of iterations to boost the weak classifiers, which is also the number of weak classifiers in the ensemble (T). The iterations must an INTEGER in the range [2, 200]. The default value is 20. |
| NumSplits |
Optional |
Specifies the number of splits to try for each attribute in the node splitting. The splits must an INTEGER. The default value is 10. |
| ApproxSplits |
Optional |
Specifies whether to use approximate percentiles. The default value is 'true'. |
| SplitMeasure |
Optional |
Specifies the type of measure to use in node splitting. The default value is 'gini'. |
| MaxDepth |
Optional |
Specifies the maximum depth of the tree. The max_depth must an INTEGER in the range [1, 10]. The default value is 3. |
| MinNodeSize |
Optional |
Specifies the minimum size of any particular node within each decision tree. The min_node_size must an INTEGER. The default value is 100. |
| DropOutputTable |
Optional |
Specifies whether to drop output_table if it exists. The default value is 'false'. |