- OutputTable
- Specify the name of the output table where the function stores the predictive model it creates.
- AttributeNameColumns
- Specify the names of AttributeTable columns that contain the data attributes.
- AttributeValueColumns
- Specify the names of AttributeTable columns that contain the data values.
- IDColumns
- Specify the names of the AttributeTable and ResponseTable columns that specify the identifier of the instance.
- ResponseColumn
- Specify the name of the ResponseTable column that contains the responses (labels) of the data.
- CategoricalEncoding
- [Optional with CategoricalAttributeTable, disallowed otherwise.] Specify algorithm for encoding categorical columns:
Option Description GrayCode Recommended when accuracy is critical. Depending on available memory, out-of-memory errors can occur if a categorical column has more than about 20 unique levels, even with a small data set. Hashing Optimizes calculation speed and minimizes memory use, possibly decreasing accuracy. - IterNum
- [Optional] Specify the number of iterations to boost the weak classifiers, which is also the number of weak classifiers in the ensemble (T). The iterations must be an INTEGER in the range [2, 200].
- NumSplits
- [Optional] Specify the number of splits to try for each attribute in the node splitting. The splits must be an INTEGER.
- ApproxSplits
- [Optional] Specify whether to use approximate percentiles.
- SplitMeasure
- [Optional] Specify the type of measure to use in node splitting.
- MaxDepth
- [Optional] Specify the depth of each weak classifier. The max_depth must be an INTEGER in the range [1, 10].
- MinNodeSize
- [Optional] Specify the minimum size of any node within each decision tree. The min_node_size must be an INTEGER.
- OutputProb
- [Optional] Specify whether to output the probability distributions for each node.