Data sets that have labels can be classified using the naïve bayes motode. A simple method of classification without an iteration process so that the process in it becomes faster than other algorithms that require the iteration process to determine the model. The naïve bayes process refers to the Bayes theory.
Naive bayes are used to specify labeled data sets based on bayes theory. This theory assumes that each attribute stands alone or is not bound by any other attribute. The simple process is to determine a certain probability and without any iteration so the process is very simple and fast even though the dataset used has a large size.
How does naïve bayes work?
It has been mentioned above that naïve bayes refer to the bayes theory which calculates the posterior probability value of P (c | x), from P (c), P (x), and P (x | c) as shown below.
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