Update Naive_Bayes_Classifiers.md

contrib/machine-learning/Naive_Bayes_Classifiers.md

@@ -126,10 +126,11 @@ 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:
 
 $$
-P(x_i | C) = {P^{x_i}}_{i,C} (1 - P_{i, C})^{(1 - x_i)}
+P(x_i | C) = {P_{i,C}^{x_i}} (1 - P_{i, C})^{(1 - x_i)}
 $$
 
 where P(𝑖,𝐶) is the probability of feature 𝑥𝑖 being 1 in class C.
+
 ## Advantages of Naive Bayes Classifier
 * Easy to implement and computationally efficient.
 * Effective in cases with a large number of features.