Databricks Certified Professional Data Scientist Exam - Topic 4 Question 43 Discussion

Actual exam question for Databricks's Databricks Certified Professional Data Scientist exam

Question #: 43
Topic #: 4

[All Databricks Certified Professional Data Scientist Questions]

Classification and regression are examples of___________.

Asupervised learning

Bun-supervised learning

CClustering

DDensity estimation
In classification, our job is to predict what class an instance of data should fall into. Another task in machine learning is regression. Regression is the prediction of a numeric value. Most people have probably seen an example of regression with a best-fit line drawn through some data points to generalize the data points. Classification and regression are examples of supervised learning. This set of problems is known as supervised because we're telling the algorithm what to predict.

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Suggested Answer: B

by Joni at Jul 04, 2023, 05:50 AM

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Linette

3 months ago

Yup, definitely supervised learning!

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Ethan

3 months ago

I thought clustering was a separate thing?

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Ronnie

4 months ago

Wait, are you sure it's not unsupervised?

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Twana

4 months ago

Totally agree, classification and regression are key!

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Ruthann

4 months ago

Supervised learning is the way to go!

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Katie

4 months ago

I keep mixing up clustering and classification. But I think classification and regression are definitely supervised learning tasks. Just need to trust my gut on this one!

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Shenika

4 months ago

I recall a question similar to this where we had to differentiate between supervised and unsupervised learning. It definitely feels like classification and regression fit into the supervised category.

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Lashandra

5 months ago

I'm not entirely sure, but I feel like clustering is more about unsupervised learning. So, I guess that means A is the right answer?

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Gail

5 months ago

I think classification and regression are both types of supervised learning, right? I remember practicing that in our last session.

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Valentin

5 months ago

Enhanced vMotion Compatibility sounds like the most targeted solution to address the processor baseline requirement. I'll go with that.

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Bettina

5 months ago

This question seems straightforward, I'm pretty confident I can identify the correct purpose of the Head of P3O role.

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Audrie

5 months ago

Okay, I think I've got it. Profit and shareholder wealth are related, but not the same thing. I'll select the option that best captures the potential conflict between the two.

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Katy

10 months ago

Supervised learning, got it. I was a bit confused between clustering and density estimation, but this question clears that up nicely. Time to go crush this exam!

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Naomi

8 months ago

You'll do great! Just remember the difference between supervised and unsupervised learning.

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Jenifer

8 months ago

Yes, supervised learning involves providing the algorithm with labeled data to learn from.

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Fernanda

8 months ago

You've got this! Remember, supervised learning involves predicting classes and numeric values.

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Lauryn

8 months ago

That's great! Good luck on your exam!

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Zena

9 months ago

You'll do great! Just remember the difference between supervised learning and clustering.

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Fabiola

9 months ago

Yes, supervised learning involves providing the algorithm with labeled data to make predictions.

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Paz

10 months ago

You've got this! Remember, supervised learning involves predicting classes or numeric values.

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Wilda

10 months ago

That's great! Good luck on your exam!

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Kaitlyn

10 months ago

Yes, supervised learning involves predicting outcomes based on labeled data. So, the correct answer is A) supervised learning.

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Norah

10 months ago

I agree with Carlee, classification and regression are examples of supervised learning.

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Carlee

10 months ago

I think the answer is A) supervised learning.

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Son

11 months ago

Yes, supervised learning involves predicting outcomes based on labeled data. So, the correct answer is A) supervised learning.

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Nakisha

11 months ago

Ah, I see! Classification and regression are both supervised learning tasks. The key difference is that classification predicts a category or class, while regression predicts a numerical value. Easy to remember now!

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