From 6dcd8ca27c5ad0f9f100084301ba9e177766af26 Mon Sep 17 00:00:00 2001
From: Divyanshi <126316406+DivyanshiSingh00@users.noreply.github.com>
Date: Wed, 19 Jun 2024 15:16:44 +0530
Subject: [PATCH] Update Naive_Bayes_Classifiers.md

---
 contrib/machine-learning/Naive_Bayes_Classifiers.md | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/contrib/machine-learning/Naive_Bayes_Classifiers.md b/contrib/machine-learning/Naive_Bayes_Classifiers.md
index 8c9a239..6541085 100644
--- a/contrib/machine-learning/Naive_Bayes_Classifiers.md
+++ b/contrib/machine-learning/Naive_Bayes_Classifiers.md
@@ -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:
 
 $$
-P(C_k | x) = P(C_k) \prod_{i=1}^{n} P(x_i | C_k)^{x_i} (1 - P(x_i | C_k))^{(1 - x_i)}
+P(x_i | C) = {P_{i,C}}^{x_i} (1 - P_(i, C))^{(1 - x_i)}
 $$
 
-
-
-![img_4.png](img_4.png)
-
-where 𝑝(𝑖,𝐶) is the probability of feature 𝑥𝑖 being 1 in class C.
+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.