1.1 - 8.10 - AdaBoost Syntax Elements - Teradata Vantage

Teradata Vantage™ - Machine Learning Engine Analytic Function Reference

Product
Teradata Vantage
Release Number
1.1
8.10
Release Date
October 2019
Content Type
Programming Reference
Publication ID
B700-4003-079K
Language
English (United States)
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.
Default: 'GrayCode'
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].
Default: 20
NumSplits
[Optional] Specify the number of splits to try for each attribute in the node splitting. The splits must be an INTEGER.
Default: 10
ApproxSplits
[Optional] Specify whether to use approximate percentiles.
For deterministic results, do all of the following:
  • Specify ApproxSplits ('false').
  • Use the UniqueID Syntax Element.
  • For IdColumns, specify a single id_column that has a unique identifier for each observation.
Default: 'true' (nondeterministic)
SplitMeasure
[Optional] Specify the type of measure to use in node splitting.
Default: 'gini'
MaxDepth
[Optional] Specify the depth of each weak classifier. The max_depth must be an INTEGER in the range [1, 10].
Default: 3
MaxDepth does not control the depth of the final tree, as MaxDepth in DecisionTree does.
MinNodeSize
[Optional] Specify the minimum size of any node within each decision tree. The min_node_size must be an INTEGER.
Default: 100
OutputProb
[Optional] Specify whether to output the probability distributions for each node.
Specify 'true' to use the OutputProb syntax element of the AdaBoostPredict function.
Default: 'false'