A team is building a spam detection system. The team wants a probability-based identification method without complex, in-depth training from the historical data set. Which of the following methods…

The correct answer is C. Naive Baves. Naive Bayes directly computes class probabilities using simple frequency counts under the independence assumption, requiring minimal training complexity and no iterative optimization-- ideal for fast, probability-based spam detection.

Machine Learning

Question

A team is building a spam detection system. The team wants a probability-based identification method without complex, in-depth training from the historical data set. Which of the following methods would best serve this purpose?

Options

ALogistic regression
BRandom forest
CNaive Baves
DLinear regression

How the community answered

(27 responses)

A
4% (1)
C
93% (25)
D
4% (1)

Explanation

Naive Bayes directly computes class probabilities using simple frequency counts under the independence assumption, requiring minimal training complexity and no iterative optimization-- ideal for fast, probability-based spam detection.

Topics

#Naive Bayes#probabilistic classification#spam detection#supervised learning

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