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ii. Naive Bayes (NV)

Naive Bayes is a classification-based probabilistic algorithm. In order
to select the class with the highest probability, it computes the

probability of each class given the input data (Uddin et al., 2019).
Naive Bayes is computationally efficient and performs well with large

datasets. It requires a relatively small amount of training data and can

handle high-dimensional feature spaces. Additionally, it can handle
both categorical and continuous features by assuming specific

probability distributions, such as Gaussian for continuous variables.
Figure 2.10 shows the new sample instance, represented by the

"white" circle, needs to be assigned to either the "red" or "green" class

(Uddin et al., 2019)

Figure 2.10 Naïve Bayes
(Source: Uddin et al., 2019)

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