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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?

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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]


Contribute your Thoughts:

An
11 days ago
Random forest seems like the way to go here. It's great at handling small datasets and preventing overfitting.
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Francine
17 days ago
I personally prefer XGBoost as it has regularization techniques to prevent overfitting.
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Brande
18 days ago
I disagree, I believe Random forest is better because it uses multiple trees to reduce overfitting.
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Kris
24 days ago
I think Decision tree is ideal for preventing overfitting.
upvoted 0 times
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