CertNexus Exam AIP-210 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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Kandis

1 months 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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Rosenda

7 days ago

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

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Rosalia

15 days ago

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

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Lavera

1 months 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 months 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

13 days 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

18 days 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

19 days 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

27 days ago

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

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Una

1 months 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 months ago

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

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Una

1 months ago

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

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Tu

2 months ago

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

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Gilma

2 months 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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