7.00.02 - SparseSVMTrainer Arguments - Aster Analytics

Teradata Aster® Analytics Foundation User GuideUpdate 2

Product
Aster Analytics
Release Number
7.00.02
Published
September 2017
Content Type
Programming Reference
User Guide
Publication ID
B700-1022-700K
Language
English (United States)
Last Update
2018-04-17
InputTable
Specifies the name of the table or view that contains the training samples.
ModelTable
Specifies the name for the model table that the function creates (which must not exist).
SampleIDColumn
Specifies the name of the input_table column that contains the identifiers of the training samples.
AttributeColumn
Specifies the name of the input_table column that contains the attributes of the samples.
ValueColumn
[Optional] Specifies the name of the input_table column that contains the attribute values. Default behavior: Each attribute has the value 1.
LabelColumn
Specifies the name of the input_table column that contains the classes of the samples.
Cost
[Optional] Specifies the regularization parameter λ in the SVM soft-margin loss function:


The cost must be greater than 0.0. Default: 1.0.

Bias
[Optional] Specifies 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 argument when not all samples center at 0. Default: 0.0.
Hash
[Optional] Specifies whether to use hash projection on attributes. Hash projection can accelerate processing speed but can slightly decrease accuracy. Default: 'false'.
You must use hash projection if the data set has more features than fit into memory.
HashBuckets
[Optional] [Valid only with Hash ('true').] Specifies 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] Specifies the weights for different classes. If you specify a weight for a class, the cost parameter for that class is weight * cost. 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 argument.
MaxStep
[Optional] Specifies the maximum number of steps of the training process. One step means that the trainer sees each sample once. The max_step must be in the range (0, 10000]. Default: 100.
Epsilon
[Optional] Specifies the termination criterion: When the difference between the values of the loss function in two sequential iterations is less than this epsilon, the function stops. The epsilon must be greater than 0.0. Default: 0.01.
Seed
[Optional] Specifies the seed used to order the training set randomly and consistently. Use this value to cause the function to generate the same model if it is run multiple times in the same database with the same argument values. The seed must be in the range [0, 9223372036854775807]. Default: 0.