3.7.3. Naïve Bayes

It is a group of algorithms that are based on the "Bayes Theorem". They work on the principle that every pair of classifying features is independent [35].

$$P(\mathcal{U}|V) = \frac{P(V|\mathcal{U})P(\mathcal{U})}{P(V)}\tag{3}$$

#### 3.7.4. Random Forest

It is fundamentally a supervised method. It is an ensemble model which contains multiple decision trees. It collects results from all decision trees and then, based on the highest voting, makes a decision [36].

$$RFfi\_i = \frac{\sum j \in \text{all trees norm} fi\_{ij}}{T} \tag{4}$$
