7.00.02 - FellegiSunterTrainer 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 that contains the object pairs and their field-pair similarity values.
ComparisonFields
Specifies the columns of input_table to use in the field-pair similarity in the training process. If the value in the column is less than threshold_value, the field pair does not agree; otherwise, the field pair agrees. Default behavior: The threshold_value of each field is 1.
TagColumn
[Optional] If you specify this argument, the function uses supervised learning; if you omit it, the function uses unsupervised learning.

This argument specifies the name of the column that indicates whether two objects match. The column must contain only the values 'M' (matched) and 'U' (unmatched).

InitialM
[Optional] For unsupervised learning, this argument specifies the initial value of m, which is the probability that a field agrees, given that the object-pair belongs to the same object. Default: 0.9.

For supervised learning, the function ignores this argument.

InitialU
[Optional] For unsupervised learning, this argument specifies the initial value of u, which is the probability that a field agrees, given that the object-pair belongs to a different object. Default: 0.1.

For supervised learning, the function ignores this argument.

InitialP
[Optional]
For unsupervised learning, this argument specifies the initial value of p, which is the percentage of all possible object-pairs that contain the same object. Default: 0.1.

For supervised learning, the function ignores this argument.

MaxIteration
[Optional] For unsupervised learning, this argument specifies the maximum number of iterations. Default: 100.

For supervised learning, the function ignores this argument.

Eta
[Optional] For unsupervised learning, this argument specifies the tolerance of the termination criterion. At the end of each iteration, the function computes the difference between the current value of p and the value of p at the end of the previous iteration. If the difference is less than eta_value, the function terminates. Default: 1*10-5.
Lambda
[Optional]
Specifies the Type I (false negative) error, which occurs if an unmatched comparison is erroneously linked. Default: 0.9.
Mu
[Optional] Specifies the Type II (false positive) error, which occurs if a matched comparison is erroneously not linked. Default: 0.9.
Lambda and Mu determine the values of the model properties lower_bound and upper_bound. For details, see: Fellegi, Ivan; Sunter, Alan (December 1969). "A Theory for Record Linkage" Journal of the American Statistical Association 64