The Naïve Bayes Model is a probabilistic classifier based on Bayes’ Theorem.
- It is used for classification problems by calculating the probability of different outcomes based on prior knowledge.
- This model is called “naïve” because it assumes that all features (predictors) are independent of each other, which is often not the case in real-world data. Despite this simplification, Naïve Bayes performs well in many practical applications.
Applications of Naïve Bayes Model:
🔹 Spam Email Filtering 📩
- Classifies emails as spam or not spam based on keywords and patterns.
- Example: If an email contains words like “lottery” or “prize”, it is more likely to be spam.
🔹 Sentiment Analysis 💬
- Determines whether a text or review has a positive, negative, or neutral sentiment.
- Example: Analyzing customer reviews for product feedback.
🔹 Medical Diagnosis 🏥
- Helps in classifying diseases based on symptoms.
- Example: If a patient has fever, cough, and sore throat, Naïve Bayes can classify the disease as flu.
Advantages of Naïve Bayes
✅ Fast and efficient for large datasets.
✅ Works well even with small training data.
✅ Performs well in text classification problems (e.g., spam filtering, sentiment analysis).
✅ Handles multi-class classification problems easily.
Limitations of Naïve Bayes
❌ Assumes independence of features, which is often not realistic.
❌ Struggles with datasets where features are highly dependent on each other.
