CertNexus AIP-210 Exam - Topic 6 Question 15 Discussion

Actual exam question for CertNexus's AIP-210 exam

Question #: 15
Topic #: 6

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?

AMean squared error

BPrecision and accuracy

CPrecision and recall

DRecall and explained variance

Show Suggested Answer

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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Chuck

3 months ago

Definitely C! Can't rely on accuracy with only 1% having the disease.

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Goldie

3 months ago

Wait, isn't recall more important than precision in this case?

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Georgeanna

4 months ago

I thought mean squared error was enough for any model?

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Miesha

4 months ago

Totally agree, accuracy can be misleading here.

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Glenna

4 months ago

Precision and recall are key for imbalanced datasets!

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Karrie

4 months ago

I practiced a similar question where precision and recall were emphasized for rare events, so I'm leaning towards C as well.

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Merrilee

4 months ago

I think recall is really important in medical diagnoses, but I can't recall if explained variance is relevant here. D seems off to me.

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Penney

5 months ago

I'm not entirely sure, but I feel like accuracy could be misleading here since the disease is so rare. Maybe B isn't the right answer?

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Mariko

5 months ago

I remember we discussed how precision and recall are crucial for imbalanced datasets, so I think C might be the best choice.

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Anglea

5 months ago

I'm pretty confident that precision and recall are the way to go for this type of problem. Accuracy can be misleading when you have a skewed class distribution, so those metrics will give you a much better sense of how well the model is identifying the disease cases.

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Jaime

5 months ago

Okay, I've got this. With such a small percentage of the population having the disease, measures like precision and recall will be crucial to evaluate the model's performance. Mean squared error and explained variance don't seem as relevant here.

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Levi

5 months ago

Hmm, I'm a bit unsure about this one. I was thinking precision and accuracy might work well, but given the low prevalence of the disease, maybe precision and recall would be better. I'll have to think this through more carefully.

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Alona

5 months ago

This seems like a tricky question. With only 1% of the population having the disease, I think precision and recall would be the best measures to use. Accuracy might not be as useful since the majority of the population doesn't have the disease.

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Corrie

5 months ago

I think option D with wrong answers in italics is the most accessible. That visual distinction seems like it would be clear and easy to distinguish. I'm leaning towards that one.

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Joaquin

5 months ago

Okay, I've got a strategy here. I'll eliminate the options that don't seem relevant to SIP and RTP, then try to match the remaining components to the question.

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Devorah

5 months ago

I think GPU support might be the most crucial since CAD applications rely heavily on graphics rendering.

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Cordelia

5 months ago

Ah, this is a good one. I remember learning about this in class. I think option D is the most comprehensive answer, requiring the processor to get the controller's consent and ensure the sub-processor meets the same data processing obligations.

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Lucia

5 months ago

I think this is about comprehensive training, so I'm leaning towards including real-life examples. That seems most practical for understanding actual money laundering risks.

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Lorrine

10 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

9 months ago

C: Definitely, accuracy alone can be misleading in this case.

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Lashunda

9 months ago

B: Yeah, with such a rare disease, precision and recall are crucial.

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Troy

9 months ago

A: Precision and recall would be the best measures to evaluate the model.

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Kattie

10 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

9 months ago

C: Mean squared error wouldn't be as useful in this case with the imbalanced dataset.

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Glory

9 months ago

B: I agree, we need to focus on those to evaluate the classifier properly.

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Claudio

10 months ago

A: Precision and recall are the best measures for this model.

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Renea

10 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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