Databricks-Certified-Professional-Data-Scientist Exam Dumps : Databricks Certified Professional Data Scientist Exam

Explanation :

The difference between their properties can be promptly summarized as follows:

Table Description automatically generated

In this problem you have been given high-dimensional independent variables like yes, no: test results etc. and you have to predict either valid or not valid (One of two). So all of the below technique can be applied to this problem.

Support vector machines Naive Bayes Logistic regression Random decision forests

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.