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Listening Quiz: Rủi Ro Của Việc Sử Dụng Dữ Liệu Thiên Vị Trong Mô Hình – Easy Level
Th6 14, 2024
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bởi
Anna Phạm
trong
Tiếng Anh dành cho Nhà khoa học dữ liệu (English for Data Scientist)
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1.
What is the main topic of the conversation?
How to train a model using very little data
Why models always make fair decisions
How to choose the fastest computer program
The risks of biased or limited data in a model
Explanation:
Laura and William discuss that a model learned from little data, may not work well, can be unfair, and can learn bad patterns if the data is biased.
2.
According to Laura, what can happen if the data is biased?
The model stops working completely
The model learns bad patterns
The model uses more data automatically
The model becomes faster
Explanation:
Laura says, ‘Then the model learns bad patterns’ in response to William’s question about biased data.
3.
What does Laura say they need to fix the problem?
A new speaker to explain the model
Less data and fewer rules
Only biased data
More data and checks
Explanation:
Laura clearly answers, ‘We need more data and checks.’
4.
What does William most likely understand by the end of the conversation?
Little data always makes the model fair
Checks are unnecessary if the data is large
Good data is important for a good model
Biased data improves model decisions
Explanation:
William says, ‘So, we must choose data carefully,’ which shows he understands that good data is important for good models.
5.
Why does Laura mention that the model learned from little data?
To say the model is already perfect
To show why the model may not work well
To explain that the model needs no checks
To prove the model can only make correct decisions
Explanation:
Laura brings up the small amount of data as the reason she worries the model may not work well and could make unfair decisions.
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