Discuss CertNexus AIP-210 Exam Topic 2 Question 34

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.

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.

Get Premium AIP-210 Questions as Interactive Web-Based Practice Test or PDF Real Exam Format Detailed Explanations Instant Access Get Your Practice Test

33 comments · newest first

Kristin 10 months ago

B and E are spot on for sure!

Jeff 11 months ago

Wait, are they really called 'black box' models? That seems off.

Tennie 11 months ago

E makes sense, sometimes you gotta trade off accuracy.

Theodora 11 months ago

A is misleading, not all interpretable models are binary.

Reyes 11 months ago

B is definitely true, stakeholders love clarity!

Georgiann 12 months ago

I recall that 'black box' models refer to complex ones, so C definitely doesn't apply to highly interpretable models.

Kimbery 12 months ago

I practiced a question similar to this, and I think E makes sense because sometimes simpler models sacrifice accuracy for clarity.

Billye 12 months ago

I'm not entirely sure, but I feel like A could be misleading since interpretability isn't limited to binary classifiers.

Myrtie 1 year ago

I remember that highly interpretable models are often easier to explain to stakeholders, so I think B might be one of the correct answers.

Amalia 1 year ago

This is a good test of our understanding of model interpretability. I'll need to draw on my knowledge of the pros and cons of different model types.

Raul 1 year ago

I feel pretty confident about this one. Interpretable models are usually simpler and easier to explain, so I'll go with those options.

Jamie 1 year ago

Okay, I've got a strategy here. I'll focus on the key characteristics of interpretable models and try to identify the two that best fit the description.

Hui 1 year ago

Hmm, I'm a bit unsure about this. I know interpretable models are important, but I'm not sure I fully understand the differences between the options.

Shayne 1 year ago

This looks like a tricky one. I'll need to think carefully about the trade-offs between interpretability and model performance.

Raylene 1 year 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!

Dyan 1 year ago

↳ replying to Raylene

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

Tula 1 year ago

↳ replying to Raylene

A) They are usually binary classifiers.

Rosita 1 year 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.

Marisha 1 year ago

↳ replying to Rosita

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

Antonio 1 year ago

↳ replying to Rosita

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

Billye 1 year ago

↳ replying to Rosita

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

Frederica 1 year 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.

Charlena 2 years ago

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

Denae 2 years ago

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

Margurite 1 year ago

↳ replying to Denae

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

Joni 1 year ago

↳ replying to Denae

I agree, black box models are definitely not interpretable.

Valentin 1 year ago

↳ replying to Denae

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

Lillian 1 year ago

↳ replying to Denae

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

Vallie 1 year ago

↳ replying to Denae

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

Rupert 1 year ago

↳ replying to Denae

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

Ashanti 2 years 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.

Eric 2 years ago

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

Van 2 years ago

I think the true statements are B and E.

CertNexus AIP-210 Exam - Topic 2 Question 34 Discussion