1.1 - 8.10 - SVMSparse 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)
ModelTable
Specify the name of the model table (which must not exist).
IDColumn
Specify the name of the InputTable column that contains the identifiers of the training samples.
AttributeNameColumn
Specify the name of the InputTable column that contains the attributes of the samples.
AttributeValueColumn
[Optional] Specify the name of the InputTable column that contains the attribute values.
Default behavior: Each attribute has the value 1.
ResponseColumn
Specify the name of the InputTable column that contains the classes of the samples.
RegularizationLambda
[Optional] Specify the regularization parameter λ in the SVM soft-margin loss function:

Formula for SVM soft-margin loss function used by RegularizationLamba syntax element in Machine Learning Engine function SVMDense
The lambda must be greater than 0.0.
Default: 1.0
Bias
[Optional] Specify whether to add another dimension containing the bias value b. The bias must be nonnegative. If bias is greater than 0, the function converts each sample in the training set to ( , b). Use this syntax element when not all samples center at 0.
Default: 0.0
HashProjection
[Optional] Specify whether to use hash projection on attributes. Hash projection can accelerate processing speed but can slightly decrease accuracy.
You must use hash projection if the data set has more features than fit into memory.
Default: 'false'
HashBuckets
[Optional] [Valid only with HashProjection ('true').] Specify the number of buckets for hash projection. Usually, the function can determine the appropriate number of buckets from the scale of the input data set. However, if the data set has many features, you might have to specify buckets_number to accelerate the function.
Default behavior: The function determines the appropriate number of buckets from the scale of the input data set.
ClassWeights
[Optional] Specify the weights for different classes. If you specify a weight for a class, the function multiplies the value of lambda used for that class by weight. A weight larger than 1 often increases the accuracy of class; however, it may decrease global accuracy.
Default behavior: The function assigns weight 1.0 to any class not assigned a weight in this syntax element.
MaxIterNum
[Optional] Specify the maximum number of steps of the training process. One step means that the trainer sees each sample once. The max_iteration_number must be in the range (0, 10000].
Default: 100
StopThreshold
[Optional] Specify the termination criterion: When the difference between the values of the loss function in two sequential iterations is less than this threshold, the function stops. The threshold must be greater than 0.0.
Default: 0.01
Seed
[Optional] Specify the random seed the algorithm uses for repeatable results. The algorithm uses the seed to order the training set randomly and consistently. The seed must be a nonnegative LONG value.
For repeatable results, use both the Seed and UniqueID syntax elements. For more information, see Nondeterministic Results and UniqueID Syntax Element.
Default: 0