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

2 months 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

3 months 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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Jennie

2 months ago

FDP sounds intense, I'll stick with the classic choice as well.

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Edelmira

2 months ago

Yeah, PDP is a reliable tool for gaining insights.

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Alona

2 months ago

I prefer C too, it's a classic choice.

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Markus

3 months ago

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

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Alyce

3 months ago

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

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Wenona

3 months ago

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

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Layla

3 months 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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Jamal

2 months ago

Definitely, PDP is a useful tool for analyzing the relationship between features and predictions.

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Bo

3 months ago

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

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Anglea

3 months ago

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

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Barabara

3 months 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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