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Google Professional Data Engineer Exam - Topic 3 Question 41 Discussion

Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set. You want to increase the AUC of the model. What should you do?
A) Perform hyperparameter tuning
B) Train a classifier with deep neural networks, because neural networks would always beat SVMs
C) Deploy the model and measure the real-world AUC; it's always higher because of generalization
D) Scale predictions you get out of the model (tune a scaling factor as a hyperparameter) in order to get the highest AUC

Google Professional Data Engineer Exam - Topic 3 Question 41 Discussion

Actual exam question for Google's Professional Data Engineer exam
Question #: 41
Topic #: 3
[All Professional Data Engineer Questions]

Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set. You want to increase the AUC of the model. What should you do?

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Suggested Answer: A

by Alyce at May 09, 2022, 08:01 AM

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Leonor
10 months ago
Definitely try hyperparameter tuning first!
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Margurite
10 months ago
Scaling predictions won't really help with AUC improvement.
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Dorinda
11 months ago
Surprised that people think real-world AUC is always higher.
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Barney
11 months ago
I disagree, deep learning is the way to go!
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Ronna
11 months ago
Hyperparameter tuning is key for better AUC!
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Lashanda
11 months ago
Scaling predictions sounds a bit off to me; I don't recall that being a common method for improving AUC in our practice questions.
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Zita
11 months ago
I think deploying the model to measure real-world AUC is a bit risky; I doubt it would always be higher than validation metrics.
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Lavonna
11 months ago
I'm not sure if switching to deep neural networks is the best approach; I feel like SVMs can still perform well with the right tweaks.
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Vicki
11 months ago
I remember we discussed hyperparameter tuning in class; it seems like a good way to potentially improve the AUC.
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Tamra
11 months ago
Hmm, I'm a bit confused on the best approach here. Should we just do a quick acceptance test on the off-the-shelf systems and then jump straight to an end-to-end test? Or is there more we need to do?
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Lucina
11 months ago
Reasonless check? That doesn't sound like a real validation method. I'm going to rule that one out.
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Krystal
11 months ago
This looks like a tricky one. I think I'll need to carefully review the options and consider which tool would be best for identifying slow-performing visualizations in a Power BI report.
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Cora
11 months ago
I think the best approach is to really dive into the Technology domain and make sure the infrastructure and technology capabilities are mapped out in detail. That seems to be the core focus of this project.
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Kasandra
11 months ago
Hmm, I'm a bit unsure about the differences between routers and switches. I'll need to review that before attempting this.
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