CertNexus Exam AIP-210 Topic 2 Question 34 Discussion

Actual exam question for CertNexus's AIP-210 exam

Question #: 34
Topic #: 2

[All AIP-210 Questions]

Which of the following statements are true regarding highly interpretable models? (Select two.)

AThey are usually binary classifiers.

BThey are usually easier to explain to business stakeholders.

CThey are usually referred to as 'black box' models.

DThey are usually very good at solving non-linear problems.

EThey usually compromise on model accuracy for the sake of interpretability.

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 Malinda at Nov 16, 2024, 11:36 AM

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Raylene

2 months ago

Hey, at least with interpretable models, we can blame the model when it gets something wrong. No more hiding behind that 'black box' nonsense!

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Dyan

1 months ago

B) They are usually easier to explain to business stakeholders.

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Tula

1 months ago

A) They are usually binary classifiers.

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Rosita

2 months ago

I'm going to have to disagree with A. Interpretable models can come in many forms, not just binary classifiers. B and E are my picks.

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Marisha

4 days ago

I also think E is true, sometimes interpretability is prioritized over accuracy.

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Antonio

29 days ago

I think B is true because it's important to be able to explain the model to stakeholders.

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Billye

1 months ago

I agree with you, interpretable models can definitely come in various forms.

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Frederica

2 months ago

Haha, D is definitely wrong. Interpretable models are great for linear problems, but not so much for non-linear ones. B and E are the way to go.

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Charlena

2 months ago

I disagree. I think the true statements are A and D. They are usually binary classifiers and good at solving non-linear problems.

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Denae

2 months ago

Wait, what? C can't be right, black box models are the opposite of interpretable models. I'd go with B and E.

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Margurite

24 days ago

Yeah, I think B and E are the correct statements about highly interpretable models.

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Joni

25 days ago

I agree, black box models are definitely not interpretable.

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Valentin

25 days ago

It's clear that C is not correct. B and E are the most suitable choices for interpretable models.

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Lillian

27 days ago

I agree, B and E are the most logical options for highly interpretable models.

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Vallie

2 months ago

Yeah, black box models are definitely not interpretable. B and E make more sense.

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Rupert

2 months ago

I think you're right, C doesn't make sense. B and E seem like the best choices.

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Ashanti

2 months ago

I think B and E are the correct answers. Highly interpretable models are easier for business stakeholders to understand, but they often trade-off accuracy for that interpretability.

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Eric

2 months ago

I agree with Van. Highly interpretable models are easier to explain and may compromise on accuracy.

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Van

3 months ago

I think the true statements are B and E.

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