Free CompTIA DataX DY0-001 Actual Exam Questions - Question 6 Discussion
method without complex, in-depth training from the historical data set. Which of the following
methods would best serve this purpose?
It’s C because Naive Bayes is known for quick training and clear probabilistic output.
A/C? Logistic regression is also probability-based and simpler than random forests, but it might need more training effort than Naive Bayes. Naive Bayes still seems best for minimal training and probabilistic output.
Option C makes sense since Naive Bayes is straightforward and fast, unlike the others.
C imo. Naive Bayes makes strong independence assumptions that simplify training and work well for text classification like spam detection. The other options need more complex training or aren’t probability-based classifiers.
C imo. Naive Bayes is pretty straightforward, uses probabilities, and doesn’t need heavy training like random forests or logistic regression. Seems like a good fit for a quick spam filter without too much hassle.