You work as a machine learning specialist for a retail chain that has recently purchased another retail chain and is in the process of merging the two chain's systems.
Both retail chains have customer databases.
Some of the firm's customers overlap, meaning that the same customer registered with both chains in the past.
When merging the customer data stores of the two, presently merged retail chains, you need to link duplicate customer data to have one accurate customer data source. You have been assigned to create the new customer data source for the presently merged retail chain.
Instead of trying to find duplicate customer data manually through traditional programming techniques, you have decided to use machine learning techniques to solve the problem. You have determined that the AWS Glue Machine Learning FindMatches Transform is the best solution to this problem.
Knowing that incorrectly linking what appear to be duplicate customers must be avoided at all costs, how should you configure the AWS Glue FindMatches ML Transform parameters to achieve the most efficient and accurate duplicate customer detection process?
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: A.
Option A is correct.
Setting the FindMatches precision-recall parameter to ‘precision' minimizes false positives (when you don't have a match of a duplicate customer but mark it as a match mistakenly)
This is what you want.
Setting the FindMatches accuracy-cost parameter to ‘accuracy' maximizes the transform accuracy of finding matching records as duplicate.
This is also what you want.
Option B is incorrect.
Setting the FindMatches precision-recall parameter to ‘precision' minimizes false positives (when you don't have a match of a duplicate customer but mark it as a match mistakenly)
This is what you want.
But, setting the accuracy-cost parameter to ‘lower cost' favors cost or the speed of running the transform at the expense of the transform's accuracy.
This may make your transform more performant, but your primary concern is avoiding linking customers incorrectly.
So you should set the accuracy-cost parameter to ‘accuracy'.
Option C is incorrect.
Setting the FindMatches precision-recall parameter to ‘recall' minimizes false negatives (when you have a match of a duplicate customer but fail to detect it)
This may cause customer frustration, but your primary concern is avoiding linking customers incorrectly.
Option D is incorrect.
Setting the FindMatches precision-recall parameter to ‘recall' minimizes false negatives (when you have a match of a duplicate customer but fail to detect it)
This may cause customer frustration, but your primary concern is avoiding linking customers incorrectly.
Reference:
Please see the AWS Glue developer guide titled Machine Learning Transforms in AWS Glue, and the AWS Glue developer guide titled Tuning Machine Learning Transforms in AWS Glue.
The AWS Glue FindMatches Machine Learning Transform is used for identifying matching records across two datasets, which can be used for deduplicating customer data. The transform works by applying a machine learning model to the datasets and returns a table with the matching records.
To configure the AWS Glue FindMatches Transform parameters to achieve the most efficient and accurate duplicate customer detection process, we need to consider two key parameters:
Precision-recall parameter: This parameter determines the trade-off between precision (the fraction of true matches among the identified matches) and recall (the fraction of true matches that are correctly identified).
Accuracy-cost parameter: This parameter determines the trade-off between accuracy (the fraction of correctly identified matches) and cost (the time and resources required to identify the matches).
Given that incorrectly linking what appear to be duplicate customers must be avoided at all costs, we need to prioritize precision over recall. This means that we want to avoid linking records that are not true matches, even if it means potentially missing some true matches.
Option A suggests setting the precision-recall parameter to ‘precision' and the accuracy-cost parameter to ‘accuracy'. This is a good configuration as it prioritizes precision, which is our main concern. By setting the accuracy-cost parameter to ‘accuracy', we ensure that the model will use the most resources possible to achieve the highest level of accuracy.
Option B suggests setting the precision-recall parameter to ‘precision' and the accuracy-cost parameter to ‘lower cost'. While prioritizing precision is still a good approach, setting the accuracy-cost parameter to ‘lower cost' may compromise the accuracy of the matching process, which is not acceptable given the constraints of the problem.
Option C suggests setting the precision-recall parameter to ‘recall' and the accuracy-cost parameter to ‘accuracy'. This is not a good configuration as it prioritizes recall over precision, which can result in linking records that are not true matches.
Option D suggests setting the precision-recall parameter to ‘recall' and the accuracy-cost parameter to ‘lower cost'. This configuration is similar to option C and is not a good choice as it also prioritizes recall over precision, which can lead to linking records that are not true matches.
Therefore, option A is the best configuration for the AWS Glue FindMatches ML Transform parameters to achieve the most efficient and accurate duplicate customer detection process, given the constraints of the problem.