Databricks Exam Databricks-Certified-Professional-Data-Scientist Topic 5 Question 36 Discussion

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

Question #: 36
Topic #: 5

[All Databricks-Certified-Professional-Data-Scientist Questions]

Which of the following metrics are useful in measuring the accuracy and quality of a recommender system?

ACluster Density

BSupport Vector Count

CMean Absolute Error

DSum of Absolute Errors
The MAE measures the average magnitude of the errors in a set of forecasts, without considering their direction. It measures accuracy for continuous variables. The equation is given in the library references. Expressed in words, the MAE is the average over the verification sample of the absolute values of the differences between forecast and the corresponding observation. The MAE is a linear score which means that all the individual differences are weighted equally in the average.
The sum of absolute errors is a valid metric, but doesn't give any useful sense of how the recommender system is performing.
Support vector count and cluster density do not apply to recommender systems.
MAE and AUC are both valid and useful metrics for measuring recommender systems.

Show Suggested Answer

Suggested Answer: C

by Audry at Jan 05, 2023, 03:16 AM

Limited Time Offer

25%

Off

Get Premium Databricks-Certified-Professional-Data-Scientist Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

Glendora

9 days ago

C) Mean Absolute Error is the way to go. It's a straightforward and widely used metric for evaluating recommender systems. Anything else is just a distraction, like trying to recommend a movie based on the number of popcorn kernels in the theater.

upvoted 0 times

...

Cassie

13 days ago

Cluster Density? Support Vector Count? These sound more like metrics for machine learning models, not recommender systems. I'm going with C) Mean Absolute Error.

upvoted 0 times

...

Leota

14 days ago

I prefer D) Sum of Absolute Errors, it gives a better sense of performance.

upvoted 0 times

...

Justine

16 days ago

I'm torn between C) Mean Absolute Error and D) Sum of Absolute Errors. Both sound like they could be useful, but I'm not sure which one is more commonly used for recommender systems.

upvoted 0 times

...

Ty

17 days ago

C) Mean Absolute Error seems like the right choice. It measures the accuracy of a recommender system by calculating the average deviation between predicted and actual values.

upvoted 0 times

...

Craig

20 days ago

I agree with Alison, MAE is a good metric for continuous variables.

upvoted 0 times

...

Alison

21 days ago

I think C) Mean Absolute Error is useful for measuring accuracy.

upvoted 0 times

...