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Update Naive_Bayes_Classifiers.md

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@ -126,14 +126,10 @@ Used for binary/boolean features, where features represent binary occurrences (e
* Formula: The likelihood of the features given the class is computed using the Bernoulli distribution formula: * Formula: The likelihood of the features given the class is computed using the Bernoulli distribution formula:
$$ $$
P(C_k | x) = P(C_k) \prod_{i=1}^{n} P(x_i | C_k)^{x_i} (1 - P(x_i | C_k))^{(1 - x_i)} P(x_i | C) = {P_{i,C}}^{x_i} (1 - P_(i, C))^{(1 - x_i)}
$$ $$
where $$ P_(𝑖,𝐶) $$ is the probability of feature 𝑥𝑖 being 1 in class C.
![img_4.png](img_4.png)
where 𝑝(𝑖,𝐶) is the probability of feature 𝑥𝑖 being 1 in class C.
## Advantages of Naive Bayes Classifier ## Advantages of Naive Bayes Classifier
* Easy to implement and computationally efficient. * Easy to implement and computationally efficient.
* Effective in cases with a large number of features. * Effective in cases with a large number of features.