7.00.02 - Output - Aster Analytics

Teradata Aster® Analytics Foundation User GuideUpdate 2

Product
Aster Analytics
Release Number
7.00.02
Release Date
September 2017
Content Type
Programming Reference
User Guide
Publication ID
B700-1022-700K
Language
English (United States)

The FellegiSunterTrainer function has one output table, which is a model table that is input to the function FellegiSunterPredict. The following table shows the schema of the output table.

FellegiSunterTrainer Output (Model) Table Schema
Column Name Data Type Description
_key VARCHAR Model property name.
_value VARCHAR Model property value.
FellegiSunterTrainer Model Properties
Property Name Data Type Description
is_supervised BOOLEAN Has the value 'true' for supervised learning and 'false' for unsupervised learning.
comparison_field_cnt INTEGER Count of comparison fields, equal to the length of the list specified by the ComparisonFields argument.
comparison_field_name_i VARCHAR Name of comparison field i, where i is in the range [0, comparison_field_cnt-1].

The table has a column for each comparison field.

comparison_field_threshold_i DOUBLE PRECISION Threshold of comparison field i, where i is in the range [0, comparison_field_cnt-1]. If the similarity value exceeds this value, the two objects agree on field i.

The table has a column for each comparison field.

m_i DOUBLE PRECISION Probability that the two objects agree on field i, given that the object pair matches, where i is in the range [0, comparison_field_cnt-1].
u_i DOUBLE PRECISION Probability that the two objects agree on field i, given that the object pair does not match, where i is in the range [0, comparison_field_cnt-1].
p DOUBLE PRECISION Percentage of object pairs that contain the same object. This column appears only in output for unsupervised learning.
lower_bound DOUBLE PRECISION If the weight of an object pair is less than lower bound, the objects do not match.
upper_bound DOUBLE PRECISION If the weight of an object pair is greater than upper bound, the objects match.
lambda DOUBLE PRECISION Type I (false negative) error, which occurs if an unmatched comparison is erroneously linked.
mu DOUBLE PRECISION Type II (false positive) error, which occurs if a matched comparison is erroneously not linked.
time_used DOUBLE PRECISION Time that the function used to learn the model parameters.