Databricks Exam Databricks-Certified-Professional-Data-Scientist Topic 4 Question 31 Discussion

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

Question #: 31
Topic #: 4

[All Databricks Certified Professional Data Scientist Exam Questions]

While working with Netflix the movie rating websites you have developed a recommender system that has produced ratings predictions for your data set that are consistently exactly 1 higher for the user-item pairs in your dataset than the ratings given in the dataset. There are n items in the dataset. What will be the calculated RMSE of your recommender system on the dataset?

Dn/2
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. RMSE is calculated as the square root of the mean of the squares of the errors. The error in every case in this example is 1. The square of 1 is 1 The average of n items with value 1 is 1 The square root of 1 is 1 The RMSE is therefore 1

Show Suggested Answer

Suggested Answer: A

by Yolando at Oct 11, 2022, 08:48 PM

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

Currently there are no comments in this discussion, be the first to comment!