- OutputTable
- Specify the name of the output table where the function stores the predictive model it creates.
- AttributeNameColumns
- Specify the names of attribute table columns that contain the data attributes.
- AttributeValueColumns
- Specify the names of attribute table columns that contain the data values.
- IDColumns
- Specify the names of the columns in the response and attribute tables that specify the identifier of the instance.
- ResponseColumn
- Specify the name of the response table column that contains the responses (labels) of the data.
- 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.With approximate percentiles, the function is nondeterministic; without them, it is deterministic. For more information, see Nondeterministic Results.
- 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.
- OutputResponseProbDist
- [Optional] Specify whether to output the probability distributions for each node.Specify 'true' to use the OutputResponseProbDist argument of the AdaBoostPredict function.