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iSQI Exam CT-AI Topic 7 Question 8 Discussion

Actual exam question for iSQI's CT-AI exam
Question #: 8
Topic #: 7
[All CT-AI Questions]

A ML engineer is trying to determine the correctness of the new open-source implementation *X", of a supervised regression algorithm implementation. R-Square is one of the functional performance metrics used to determine the quality of the model.

Which ONE of the following would be an APPROPRIATE strategy to achieve this goal?

SELECT ONE OPTION

Show Suggested Answer Hide Answer
Suggested Answer: C

A . Add 10% of the rows randomly and create another model and compare the R-Square scores of both the models.

Adding more data to the training set can affect the R-Square score, but it does not directly verify the correctness of the implementation.

B . Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.

Changing the order of input features should not significantly affect the R-Square score if the implementation is correct, but this approach is more about testing model robustness rather than correctness of the implementation.

C . Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.

This approach directly compares the performance of two implementations of the same algorithm. If both implementations produce similar R-Square scores on the same training and testing data, it suggests that the new implementation 'X' is correct.

D . Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.

Dropping data can lead to variations in the R-Square score but does not directly verify the correctness of the implementation.

Therefore, option C is the most appropriate strategy because it directly compares the performance of the new implementation 'X' with another implementation using the same algorithm and datasets, which helps in verifying the correctness of the implementation.


Contribute your Thoughts:

Veronika
3 months ago
This question is making my head spin. Can we just get a calculator and start crunching numbers? That's what real engineers do, right?
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Fidelia
2 months ago
Let's focus on comparing the R-Square scores of different models to determine the correctness of the new implementation.
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Florinda
3 months ago
No, we need to follow a systematic approach to evaluate the model's performance.
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Leah
3 months ago
Jaleesa, you're a riot! Adding or dropping rows randomly is not going to give you any meaningful insights about the algorithm implementation. Option C is definitely the way to go here.
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Amie
2 months ago
Definitely, using two different programming languages with the same algorithm and data will provide valuable insights into the performance of the new implementation.
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Omer
3 months ago
Yeah, comparing the R-Square scores of models obtained using different implementations is a solid way to evaluate the quality of the algorithm.
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Alex
3 months ago
I agree with you, option C seems like the most appropriate strategy to determine the correctness of the new implementation.
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Aracelis
3 months ago
I agree with Royce, comparing R-Square scores using different programming languages would provide a more robust evaluation.
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Jaleesa
4 months ago
Hmm, I'm not sure about that. Wouldn't it be better to just throw more data at it and see what happens? I mean, that's how I usually debug my code. Just add more rows, right?
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Keshia
3 months ago
C: Comparing the R-Square scores of models from different implementations using the same algorithm and data can provide valuable insights into the quality of the new implementation.
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Clemencia
3 months ago
B: Training various models with different input feature orders could help determine if the model is robust and reliable.
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Jaime
3 months ago
A: Adding more data might not necessarily improve the model's performance. It's important to use appropriate strategies to evaluate the correctness of the new implementation.
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Xochitl
3 months ago
C: Comparing R-Square scores using different implementations can provide valuable insights.
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Joesph
3 months ago
B: It's important to use appropriate strategies to evaluate the model's correctness.
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Telma
3 months ago
A: Adding more data might not necessarily improve the model's performance.
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Royce
4 months ago
But changing the order of input features may not necessarily help determine the correctness of the implementation.
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Sage
4 months ago
I disagree, I believe option B is more appropriate.
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Royce
4 months ago
I think option C would be a good strategy.
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Jaclyn
4 months ago
I agree with Ona. Option C is the most appropriate strategy. Comparing the results across different implementations is the best approach to ensure the correctness of the new algorithm.
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Edmond
4 months ago
I think comparing the R-Square scores from models using different programming languages is a solid strategy.
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Zack
4 months ago
Option C is definitely the way to go. It's important to compare the results from different implementations.
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Ona
4 months ago
Option C is the way to go. Comparing the R-Square scores of the same algorithm implemented in different languages is the best way to verify the correctness of the new open-source implementation.
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Leeann
3 months ago
Agreed, it's a reliable method to ensure the accuracy of the new open-source implementation.
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Celeste
3 months ago
Definitely, comparing the scores from different implementations is a good way to validate the new implementation.
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Jesusa
3 months ago
That sounds like a solid plan.
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Elly
4 months ago
C) Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.
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