Extracting matching features using tMatchModel - Cloud - 8.0

Data matching with Talend tools

Version
Cloud
8.0
Language
English
Product
Talend Big Data Platform
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Module
Talend Studio
Content
Data Governance > Third-party systems > Data Quality components > Matching components > Continuous matching components
Data Governance > Third-party systems > Data Quality components > Matching components > Data matching components
Data Governance > Third-party systems > Data Quality components > Matching components > Fuzzy matching components
Data Governance > Third-party systems > Data Quality components > Matching components > Matching with machine learning components
Data Quality and Preparation > Third-party systems > Data Quality components > Matching components > Continuous matching components
Data Quality and Preparation > Third-party systems > Data Quality components > Matching components > Data matching components
Data Quality and Preparation > Third-party systems > Data Quality components > Matching components > Fuzzy matching components
Data Quality and Preparation > Third-party systems > Data Quality components > Matching components > Matching with machine learning components
Design and Development > Third-party systems > Data Quality components > Matching components > Continuous matching components
Design and Development > Third-party systems > Data Quality components > Matching components > Data matching components
Design and Development > Third-party systems > Data Quality components > Matching components > Fuzzy matching components
Design and Development > Third-party systems > Data Quality components > Matching components > Matching with machine learning components
Last publication date
2024-02-06
You can use the labeled sample of suspect duplicate pairs as the input of the tMatchModel component.

You have to specify the set of columns the model will be built on and the column specifying the label. The algorithm will compute different measures, called features, to catch as much information as possible on this set of columns.

The tMatchModel component uses the Random forest algorithm to build the model. This algorithm is a generalization of decision trees.