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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
1 months 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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Leota
7 days ago
C: Definitely, accuracy alone can be misleading in this case.
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Lashunda
16 days ago
B: Yeah, with such a rare disease, precision and recall are crucial.
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Troy
17 days ago
A: Precision and recall would be the best measures to evaluate the model.
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Kattie
1 months 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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Leslee
11 days ago
C: Mean squared error wouldn't be as useful in this case with the imbalanced dataset.
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Glory
28 days ago
B: I agree, we need to focus on those to evaluate the classifier properly.
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Claudio
30 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
2 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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Jarvis
9 days ago
Yes, precision and recall are better measures for evaluating a model with imbalanced classes.
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Weldon
14 days ago
I think precision and recall would be more appropriate for evaluating a classifier in this case.
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Kathryn
1 months ago
You're right, mean squared error is not suitable for classification tasks.
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Shaquana
2 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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Mauricio
2 days ago
D: Recall and explained variance could also help us understand how well the model is performing.
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Donette
14 days ago
C: Mean squared error wouldn't be as useful in this case since the dataset is imbalanced.
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Renay
1 months ago
B: I agree, we need to focus on minimizing false positives and false negatives.
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Belen
1 months 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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