Snowflake Exam DSA-C02 Topic 1 Question 30 Discussion

Actual exam question for Snowflake's DSA-C02 exam

Question #: 30
Topic #: 1

[All DSA-C02 Questions]

Which of the following is a useful tool for gaining insights into the relationship between features and predictions?

Anumpy plots

Bsklearn plots

CPartial dependence plots(PDP)

DFULL dependence plots (FDP)

Show Suggested Answer

Suggested Answer: C

Partial dependence plots (PDP) is a useful tool for gaining insights into the relationship between features and predictions. It helps us understand how different values of a particular feature impact model's predictions.

by Gwenn at Sep 16, 2024, 03:15 PM

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Oretha

7 days ago

C) Partial dependence plots, no doubt. It's a great way to visualize and interpret the effects of features on the model output.

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Selma

12 days ago

Haha, Full Dependence Plots (FDP)? Is that like the over-caffeinated version of PDP? I'll stick with C, the classic choice.

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Markus

16 days ago

I believe numpy plots are also helpful in understanding the relationship between features and predictions.

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Alyce

19 days ago

I think Partial dependence plots (PDP) is the most useful tool.

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Wenona

19 days ago

I prefer sklearn plots for gaining insights into the relationship between features and predictions.

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Layla

27 days ago

Hmm, I'd have to go with C as well. PDP is a powerful tool for understanding the impact of individual features on the model's predictions.

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Bo

9 days ago

I think PDP is the way to go for gaining insights into feature predictions.

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Anglea

13 days ago

I agree, PDP is really helpful in understanding feature impact.

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Barabara

28 days ago

C) Partial dependence plots (PDP) is definitely the way to go for gaining insights into feature-prediction relationships. I used it in my last project and it was super helpful.

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