CertNexus AIP-210 Exam - Topic 1 Question 32 Discussion

Actual exam question for CertNexus's AIP-210 exam

Question #: 32
Topic #: 1

The following confusion matrix is produced when a classifier is used to predict labels on a test dataset. How precise is the classifier?

A48/(48+37)

B37/(37+8)

C37/(37+7)

D(48+37)/100

Show Suggested Answer

Suggested Answer: D

Stratification is not a valid cross-validation method, but a technique to ensure that each subset of data has the same proportion of classes or labels as the original data. Stratification can be used in conjunction with cross-validation methods such as k-fold or leave-one-out to preserve the class distribution and reduce bias or variance in the validation results. Bootstrapping, k-fold, and leave-one-out are all valid cross-validation methods that use different ways of splitting and resampling the data to estimate the performance of a machine learning model.

by Andra at Sep 17, 2024, 05:26 AM

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Margret

8 months ago

Option C seems like a solid choice too!

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Arlette

8 months ago

Wait, how can the precision be over 1? That seems off.

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Filiberto

9 months ago

Nah, I disagree, it's definitely option B.

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Marsha

9 months ago

I think option A looks right!

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Linwood

9 months ago

Precision is calculated as TP / (TP + FP).

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Tambra

9 months ago

I thought precision was just about the true positives, but I’m confused about whether we should include the false negatives in this case.

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Marla

10 months ago

I feel like option A looks familiar since it uses the true positives, but I'm not confident about the false positives part.

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Tarra

10 months ago

I remember practicing a similar question where we had to calculate precision, and I think it was something like true positives divided by the total predicted positives.

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Vallie

10 months ago

I think precision is calculated as true positives over the sum of true positives and false positives, but I'm not entirely sure which numbers to use from the matrix.

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Audrie

10 months ago

Ugh, I'm a little lost here. What exactly is precision, and how do I use this confusion matrix to figure it out?

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Arlene

10 months ago

Okay, I've seen these before. The key is to focus on the true positives and false positives to calculate the precision.

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Jose

10 months ago

Whoa, a confusion matrix - that's a new one for me. Let me think this through step-by-step.

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Ilene

10 months ago

Hmm, this looks like a classic precision calculation from a confusion matrix. I think I can handle this one.

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Frederica

10 months ago

No problem, I've got this. Precision is all about how many of the positive predictions were actually correct. Time to crunch some numbers!

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Mariann

10 months ago

I remember a practice question where we discussed how the default gateway for servers in an EPG is usually the Layer 3 out subnet address. I'm leaning toward option B.

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Kandis

1 year ago

Alright, let's do this! Precision is all about getting the right answers, not the most answers. A) is the way to go, no doubt about it.

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Art

1 year ago

User 3: Let's calculate it and see if the classifier is precise.

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Rosenda

1 year ago

User 2: Definitely, A) 48/(48+37) is the correct formula for precision.

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Rosalia

1 year ago

User 1: I agree, precision is about getting the right answers.

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Lavera

1 year ago

Hmm, I wonder if the test maker has a sense of humor. Maybe they'll throw in a 'banana' option just to see who's paying attention!

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Karol

1 year ago

I'm confident the answer is A. This is a straightforward calculation of precision, and the other options don't make sense given the information provided.

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Roxane

1 year ago

I'm glad we all agree on A. It's important to be able to interpret the results of a classifier using metrics like precision.

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Dorothy

1 year ago

That's right, A is the right choice. It's important to understand how to calculate precision from a confusion matrix.

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Ligia

1 year ago

Yes, A is the correct answer. The formula for precision is true positives divided by true positives plus false positives.

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Daisy

1 year ago

I agree, the answer is A. It's a simple calculation based on the confusion matrix.

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Una

1 year ago

I see your point, but I still think option A is the right choice because it considers both true positives and false positives.

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Rashida

1 year ago

I disagree, I believe the correct calculation is in option B.

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Una

1 year ago

I think the precision of the classifier is calculated by option A.

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Tu

1 year ago

But option A considers true positives and false positives, which are important for precision.

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Gilma

1 year ago

The correct answer is A) 48/(48+37), which represents the precision of the classifier. The confusion matrix shows the true positive and false positive counts, and precision is the ratio of true positives to the sum of true positives and false positives.

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