Databricks Exam Databricks-Certified-Professional-Data-Scientist 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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Katy

3 days 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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Kaitlyn

9 days ago

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

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Norah

11 days ago

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

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Carlee

14 days ago

I think the answer is A) supervised learning.

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Son

15 days ago

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

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Nakisha

19 days 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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Alecia

23 days ago

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

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Azzie

26 days ago

I think the answer is A) supervised learning.

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