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CertNexus Exam AIP-210 Topic 6 Question 15 Discussion

Actual exam question for CertNexus's AIP-210 exam
Question #: 15
Topic #: 6
[All AIP-210 Questions]

A classifier has been implemented to predict whether or not someone has a specific type of disease. Considering that only 1% of the population in the dataset has this disease, which measures will work the BEST to evaluate this model?

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Suggested Answer: B

A support-vector machine (SVM) is a supervised learning algorithm that can be used for classification or regression problems. An SVM tries to find an optimal hyperplane that separates the data into different categories or classes. However, sometimes the data is not linearly separable, meaning there is no straight line or plane that can separate them. In such cases, a polynomial kernel can help improve the prediction of the SVM by transforming the data into a higher-dimensional space where it becomes linearly separable. A polynomial kernel is a function that computes the similarity between two data points using a polynomial function of their features.


by Leatha at Apr 10, 2024, 03:25 AM

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Lorrine
20 days ago
I heard the disease is so rare, the model will just predict 'no disease' for everyone and still get 99% accuracy. Talk about a real 'disease' of the model!
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Lashunda
2 days ago
B: Yeah, with such a rare disease, precision and recall are crucial.
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Troy
3 days ago
A: Precision and recall would be the best measures to evaluate the model.
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Kattie
27 days ago
Ooh, explained variance? That's a new one. I wonder if the developers have been reading too many research papers lately. Stick to the classics, folks.
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Glory
13 days ago
B: I agree, we need to focus on those to evaluate the classifier properly.
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Claudio
16 days ago
A: Precision and recall are the best measures for this model.
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Renea
1 months ago
Precision and accuracy, huh? Not a bad choice, but I think recall is going to be the real MVP in this case. Gotta make sure we catch those rare disease cases.
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Melvin
1 months ago
Mean squared error? Seriously? That's for regression problems, not classification. C'mon, we're dealing with a binary outcome here.
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Kathryn
18 days ago
You're right, mean squared error is not suitable for classification tasks.
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Shaquana
1 months ago
Aha! Precision and recall are the way to go for this imbalanced dataset. Can't let those false positives or false negatives slip through the cracks.
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Renay
22 days ago
B: I agree, we need to focus on minimizing false positives and false negatives.
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Belen
27 days ago
A: Precision and recall are definitely the best measures for this type of dataset.
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Marla
2 months ago
I think mean squared error would not be suitable in this case, as it does not take into account the class imbalance.
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Felicitas
2 months ago
I agree with Josphine, since the dataset has imbalanced classes, precision and recall would be more informative.
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Josphine
2 months ago
I think precision and recall would work best.
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