CT-AI · Question #73
A data scientist is performing unsupervised learning on a set of financial records relating to previous loan applications, and trying to predict defaults on future loans. They are reporting poor…
The correct answer is D. Irrelevant data included in the account records. Irrelevant data included in the account records is less likely to contribute to poor functional performance in unsupervised learning, especially compared to missing records, missing key data (such as whether loans were granted or repaid), or inconsistent pre-processing. While…
Question
A data scientist is performing unsupervised learning on a set of financial records relating to previous loan applications, and trying to predict defaults on future loans. They are reporting poor functional performance because of data issues. Which ONE of the below is LEAST likely to be a contributory factor?
Options
- AMissing records for some accounts
- BMissing data relating to whether loans were previously granted and repaid
- CInconsistent pre-processing of some records
- DIrrelevant data included in the account records
How the community answered
(53 responses)- A13% (7)
- B8% (4)
- C4% (2)
- D75% (40)
Explanation
Irrelevant data included in the account records is less likely to contribute to poor functional performance in unsupervised learning, especially compared to missing records, missing key data (such as whether loans were granted or repaid), or inconsistent pre-processing. While irrelevant data can affect the quality of the model, missing or inconsistent data typically has a more direct negative impact on unsupervised learning models.
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