What is Naive Bayes classifier?

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The Naive Bayes classifier is a supervised machine learning algorithm based on Bayes’ theorem. It is called “naive” because it assumes that all features are independent of each other, which is rarely true in reality but works well in practice.

🔹 Bayes’ Theorem

$$P(A|B) = \frac{P(B|A) \cdot P(A)}{P(B)}$$

Here, probability of class A given feature B depends on prior probability of A and likelihood of B.

🔹 How Naive Bayes Works

1. Calculate prior probability of each class (e.g., spam vs non-spam).

2. For each feature, compute the likelihood of it appearing in a given class.

3. Apply Bayes’ theorem to calculate posterior probability for each class.

4. Assign the data point to the class with the highest probability.

🔹 Types of Naive Bayes

• Multinomial NB → For text classification (word frequencies).

• Gaussian NB → For continuous features (assumes normal distribution).

• Bernoulli NB → For binary/boolean features.

🔹 Advantages

• Fast and efficient on large datasets.

• Performs well in text classification, spam filtering, sentiment analysis.

• Requires less training data.

🔹 Limitations

• Assumes feature independence (not always realistic).

• Struggles with correlated or complex features.

👉 In short, the Naive Bayes classifier is a simple yet powerful probabilistic model that applies Bayes’ theorem with independence assumptions, making it highly effective for text and categorical data problems.

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