Databricks Certified Professional Data Scientist Exam - Topic 5 Question 92 Discussion

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

Question #: 92
Topic #: 5

[All Databricks Certified Professional Data Scientist Questions]

RMSE is a useful metric for evaluating which types of models?

ALogistic regression

BNaive Bayes classifier

CLinear regression

DAll of the above
Error calculation allows you to see how well a machine learning
method is performing.
One way of determining this performance is to calculate a numerical error This number is sometimes a percent,
however it can also be a score or distance. The goal is usually to minimize an error
percent or distance:
however th goal may be to minimize or maximize a score. Encog supports the
following error calculation methods.
Sum of Squares Error (ESS)
Root Mean Square Error (RMS)
Mean Square Error (MSE) (default)
SOM Error (Euclidean Distance Error)
RMSE measures error of a predicted numeric value, and so applies to contexts like
regression and some recommender system techniques,
which rely on predicting a numeric value. It is not relevant to classification
techniques
like logistic regression and Naive Bayes, which predict categorical values.
It also is not relevant to unsupervied techniques like clustering.
The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a
frequently used measure of the
differences between values predicted by a model or an estimator and the values
actually observed. Basically,
the RMSD represents the sample standard deviation of the differences between
predicted values and observed values.
These individual differences are called residuals when the calculations are
performed over the data sample that was used for estimation,
and are called prediction errors when computed out-of-sample. The RMSD serves
to aggregate the magnitudes
of the errors in predictions for various times into a single measure of predictive
power. RMSD is a good measure of accuracy,
but only to compare forecasting errors of different models for a particular variable
and not between variables, as it is scale-dependent.

Show Suggested Answer

Suggested Answer: C

by Tu at Jan 05, 2026, 05:16 AM

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Emiko

1 day ago

RMSE is great for linear regression, right?

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Josphine

6 days ago

Wait, so it doesn't apply to logistic regression? That’s surprising!

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Levi

11 days ago

Totally agree, it doesn't work for classification!

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Gerald

17 days ago

RMSE is mainly for regression models.

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Mary

22 days ago

I thought RMSE was just for regression, but this explanation makes sense. Gotta love those fancy statistical terms!

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Derick

27 days ago

D) All of the above. RMSE is a great metric for evaluating the performance of any numeric prediction model, whether it's linear regression, recommender systems, or anything else.

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Lamar

2 months ago

Haha, I always get RMSE and RSME mixed up. Gotta remember, it's the "root mean square" error, not the "really silly made-up error."

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Cheryl

2 months ago

RMSE is definitely useful for evaluating linear regression models, but not for classification models like logistic regression or Naive Bayes.

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Dana

2 months ago

C) Linear regression

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Reita

2 months ago

I feel like RMSE is a good measure for numeric predictions, but I wonder if it could ever be useful in a classification context.

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Farrah

2 months ago

I practiced a similar question about error metrics, and I recall that RMSE is not suitable for classification models like Naive Bayes.

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Rachael

2 months ago

I think RMSE is definitely relevant for linear regression, but I’m a bit confused about its application in logistic regression.

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Dominic

3 months ago

I remember RMSE is mainly used for regression models, but I'm not sure if it applies to all types of regression.

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Chaya

3 months ago

The question provides a good overview of RMSE and when it's useful. Based on that, I'd say the answer is C) Linear regression, since RMSE is for evaluating the accuracy of numeric predictions, which is what linear regression does.

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Coleen

3 months ago

I think I understand the concept of RMSE, but I'm not sure if it applies to all types of regression models or just certain ones. I'll need to double-check the details in the question.

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Amos

3 months ago

Okay, the key here is that RMSE is specifically for evaluating regression models, not classification models like logistic regression or Naive Bayes. So the answer is C) Linear regression.

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Eliseo

3 months ago

Hmm, I'm a bit confused about the difference between RMSE and the other error metrics mentioned. I'll need to review the details on when each one is appropriate.

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Lili

3 months ago

This seems like a straightforward question about the appropriate use of RMSE. I'm pretty confident I can answer this correctly.

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