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

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Glendora

2 months 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.

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Kassandra

1 months ago

A) Cluster Density, B) Support Vector Count, and D) Sum of Absolute Errors are not relevant for measuring the accuracy and quality of a recommender system.

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Corrie

2 months ago

C) Mean Absolute Error is the way to go. It's a straightforward and widely used metric for evaluating recommender systems.

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Cassie

2 months 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.

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Leota

2 months ago

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

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Justine

2 months 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.

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Ty

2 months 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.

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An

1 months ago

D) Sum of Absolute Errors may not provide a clear indication of how well a recommender system is performing.

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Huey

1 months ago

C) Mean Absolute Error is indeed a useful metric for continuous variables in a recommender system.

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Kayleigh

2 months ago

A) Cluster Density and B) Support Vector Count are not relevant metrics for measuring the accuracy and quality of a recommender system.

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Johana

2 months 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.

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Craig

2 months ago

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

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Alison

2 months ago

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

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