- OutputTable
- Specify the name for the model table that the function outputs.
- OutputMessageTable
- [Optional] Specify the name for the output message table that the function outputs.
- ResponseColumn
- Specify the name of the InputTable column that contains the response variable (that is, the quantity that you want to predict).
- NumericInputs
- [Required if you omit CategoricalInputs.] Specify the names of the InputTable columns to treat as the numeric predictor variables (which must be numeric values).
- CategoricalInputs
- [Required if you omit NumericInputs.] Specify the names of the InputTable columns to treat as the categorical predictor variables (which can be either numeric or VARCHAR values).
- CategoricalEncoding
- [Optional with CategoricalInputs, disallowed otherwise.] Specify algorithm for encoding categorical columns:
Option Description GrayCode Recommended when accuracy is critical. Depending on available memory, performance may be impacted if a categorical column has a large number (for example, 20) unique levels, even with a small data set. Hashing Optimizes calculation speed and minimizes memory use, possibly decreasing accuracy. - TreeType
- [Optional] Specify whether the analysis is a regression (continuous response variable) or a multiple-class classification (predicting result from the number of classes).
- NumTrees
- [Optional] Specify 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 data set with full coverage.
- TreeSize
- [Optional] Specify the number of rows that each tree uses as its input data set.
- MinNodeSize
- [Optional] Specify a decision tree stopping criterion; the minimum size of any node within each decision tree.
- Variance
- [Optional] Specify a decision tree stopping criterion. If the variance within any node dips below this value, the algorithm stops looking for splits in the branch.
- MaxDepth
- [Optional] Specify 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.
- Mtry
- [Optional] Specify 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] Specify a LONG value to use in determining the random seed for mtry.
- OutOfBag
- [Optional] Specify whether to output the out-of-bag estimate of error rate.
- DisplayNumProcessedRows
- [Optional] Specify whether to output the number of input rows allocated to each worker and the number of input rows processed by each worker (excluding rows skipped because they contained NULL values).
- Seed
- [Optional] Specify the random seed the algorithm uses for repeatable results. The seed must be a LONG value.For repeatable results, use both the Seed and UniqueID syntax elements. For more information, see Nondeterministic Results and UniqueID Syntax Element.
- IDColumn
- [Required with OutOfBag, optional otherwise.] Specify the name of the InputTable column that contains the row identifier.