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CertNexus Exam AIP-210 Topic 1 Question 40 Discussion

Actual exam question for CertNexus's AIP-210 exam

Question #: 40
Topic #: 1

[All AIP-210 Questions]

You have a dataset with many features that you are using to classify a dependent variable. Because the sample size is small, you are worried about overfitting. Which algorithm is ideal to prevent overfitting?

ADecision tree

BLogistic regression

CRandom forest

DXGBoost

Show Suggested Answer

Suggested Answer: B

Performance testing is a type of testing that should be performed at the production level before deploying a newly retrained model. Performance testing measures how well the model meets the non-functional requirements, such as speed, scalability, reliability, availability, and resource consumption. Performance testing can help identify any bottlenecks or issues that may affect the user experience or satisfaction with the model. Reference: [Performance Testing Tutorial: What is, Types, Metrics & Example], [Performance Testing for Machine Learning Systems | by David Talby | Towards Data Science]

by Johana at Mar 20, 2025, 11:57 AM

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Yolande

27 days ago

I'm just hoping the exam doesn't ask about overfitting the dataset to my brain. That would be a whole other problem.

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Yoko

29 days ago

I heard XGBoost is the new hot thing. It's like the avocado toast of machine learning algorithms.

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Nicholle

1 months ago

Decision tree? Are you kidding me? That's just asking for trouble with a small dataset. Definitely not the way to go.

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Merrilee

1 months ago

Hmm, I'm not sure. Maybe logistic regression would be better to avoid overfitting? It's a simpler model, but that might be the safest bet.

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Chau

16 days ago

I think logistic regression is a good choice. It's simpler and less likely to overfit.

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Brandee

1 months ago

I'd go with XGBoost. It's a powerful algorithm that can handle overfitting really well, even with limited data.

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Jaleesa

17 days ago

Random forest is also a good option for preventing overfitting.

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Edelmira

1 months ago

I agree, XGBoost is known for its ability to handle overfitting.

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Gwenn

1 months ago

XGBoost is a great choice for preventing overfitting.

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An

2 months ago

Random forest seems like the way to go here. It's great at handling small datasets and preventing overfitting.

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Lisandra

1 months ago

I agree, Random forest is known for handling small datasets well.

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Shawnda

2 months ago

Random forest is a good choice for preventing overfitting.

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Francine

2 months ago

I personally prefer XGBoost as it has regularization techniques to prevent overfitting.

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Brande

2 months ago

I disagree, I believe Random forest is better because it uses multiple trees to reduce overfitting.

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Kris

3 months ago

I think Decision tree is ideal for preventing overfitting.

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