Argument | Category | Description |
---|---|---|
InputTable | Required | Specifies the name of the table that contains the input data set. |
OutputTable | Required | Specifies the name of the output table in which the function stores the predictive model that it generates. If a table with this name exists in the database, the function drops the existing table and creates a new table with the same name. |
ResponseColumn | Required | Specifies the name of the column that contains the response variable (that is, the quantity that you want to predict). |
NumericInputs | Either NumericInputs or CategoricalInputs is required | Specifies the names of the columns that contain the numeric predictor variables (which must be numeric values). |
MaxNumCategoricalValues | Optional | Specifies the maximum number of distinct values for a single categorical variable. The max_cat_values must be a positive INTEGER. The default value is 20. A max_cat_values greater than 20 is not recommended. |
CategoricalInputs | Either NumericInputs or CategoricalInputs is required | Specifies the names of the columns that contain the categorical predictor variables (which can be either numeric or VARCHAR values). Each categorical input column can have at most max_cat_values distinct categorical values. If max_cat_values exceeds 20, the function might run out of memory, because classification trees grow rapidly as max_cat_valuesincreases. |
NumTrees | Optional | Specifies the number of trees to grow in the forest model. When specified, number_of_trees must be greater than or equal to the number of vworkers. When not specified, the function builds the minimum number of trees that provides the input dataset with full coverage. |
TreeType | Optional | Specifies whether the analysis is a regression (continuous response variable) or a multiple-class classification (predicting result from the number of classes). The default value is 'regression' if the response variable is numeric and 'classification' if the response variable is nonnumeric. |
TreeSize | Optional | Specifies the number of rows that each tree uses as its input data set. If not specified, the function builds a tree using either the number of rows on a vworker or the number of rows that fit into the vworker's memory, whichever is less. |
MinNodeSize | Optional | Specifies a decision tree stopping criterion; the minimum size of any node within each decision tree. The default value is 1. |
Variance | Optional | Specifies a decision tree stopping criterion. If the variance within any node dips below this value, the algorithm stops looking for splits in the branch. The default value is 0. |
MaxDepth | Optional | Specifies a decision tree stopping criterion. If the tree reaches a depth past this value, the algorithm stops looking for splits. Decision trees can grow to (2(max_depth+1) - 1) nodes. This stopping criteria has the greatest effect on the performance of the function. The default value is 12. |
NumSurrogates | Optional | Specifies the number of surrogate splits to evaluate for each node. The default value is 0. |
MonitorTable | Optional | Specifies the name of the table in which the function stores monitoring information. The default value is 'default_dt_monitor_table' in the current schema. |
DropMonitorTable | Optional | Specifies whether to drop the table specified by MonitorTable, if it exists. The default value is 'true'. |
Mtry | Optional | Specifies the number of variables to randomly sample from each input value. For example, if mtry is 3, then the function randomly samples 3 variables from each input at each split. The mtry must be an INTEGER. |
MtrySeed | Optional | Specifies a LONG value to use in determining the random seed for mtry. |
Seed | Optional | Specifies a LONG value to use in determining the seed for the random number generator. If you specify this value, you can specify the same value in future calls to this function and the function will build the same tree. |