DecisionTree Arguments - Teradata Vantage

Machine Learning Engine Analytic Function Reference
Product
Teradata Vantage
Release Number
8.00
1.0
Published
May 2019
Language
English (United States)
Last Update
2019-11-22
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B700-4003
lifecycle
previous
Product Category
Teradata Vantage™
OutputTable
Specify the name for the output table that is to contain the final decision tree (the model table). The output_table must not exceed 64 characters.
SaveFinalResponseTableTo
[Optional] Specify the name for the output table that is to contain the final PID and response pair from the response table and the node_id from the final single drive tree.
Default behavior: The function does not output this table.
IntermediateSplitsTable
[Disallowed with SplitsTable, optional otherwise] Specify the name for the intermediate splits table, if it is to be saved.
Default behavior: The function does not save the intermediate splits table.
AttributeNameColumns
Specify the names of the input or attribute table columns that have the attribute names.
AttributeValueColumn
Specify the name of the input or attribute table columns that have the attribute values.
ResponseColumn
Specify the name of the response table column that contains the response variable.
IDColumns
Specify the names of the columns in the response and attribute tables that specify the ID of the instance.
SplitsValueColumn
[Optional] If you specify SplitsTable, this argument specifies the name of the column that contains the split value.
Default: splits_valcol if UseApproximateSplits is 'true', attribute_value_column (the AttributeValueColumn argument value) otherwise
NumSplits
[Optional] Specify the number of splits to consider for each variable. The function does not consider all possible splits for all attributes.
Default: 10
ApproxSplits
[Optional] Specify whether to use approximate percentiles (true) or exact percentiles (false). Internally, the function uses percentile values as split values.
Default: 'true'
MinNodeSize
[Optional] Specify the decision tree stopping criterion and the minimum size of any particular node within each decision tree.
Default: 100
MaxDepth
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 up to (2(max_depth+1) - 1) nodes. This stopping criteria has the greatest effect on function performance.
Default: 5
Maximum: 60
Weighted
[Optional] Specify whether to build a weighted decision tree. If you specify 'true', you must also specify the WeightColumn argument.
Default: 'false'
WeightColumn
[Required with Weighted, otherwise optional] Specify the name of the response table column that contains the weights of the attribute values.
SplitMeasure
[Optional] Specify the impurity measurement to use while constructing the decision tree. If the tree is weighted, this value cannot be 'chisquare'.
Default: 'gini'
OutputResponseProbDist
[Optional] Specify whether to output the probability distributions for each node.
Specify 'true' to use the OutputResponseProbDist argument of the DecisionTreePredict function.
Default: 'false'
ResponseProbDistType
[Optional] Specifies the probability distribution type.
Default: 'laplace'