DM ASSIGNMENT .pptx

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1. * Introduction to Naïve Bayes *

2. *Introduction to Naïve Bayes* Naïve Bayes is a simple yet powerful probabilistic machine learning algorithm that is often used for classification tasks. It is based on Bayes' theorem, which is a fundamental concept in probability theory. Naïve Bayes is particularly popular for text classification tasks, such as spam filtering and sentiment analysis, but it can be applied to a wide range of problems .

3. Bayes' Theorem At the core of Naïve Bayes is Bayes' theorem, which describes the probability of an event based on prior knowledge of conditions that might be related to the event. The formula is as follows:

4. Bayes' Theorem where: P ( A ∣ B ) is the probability of event A given that event B has occurred. P ( B ∣ A ) is the probability of event B given that event A has occurred. P ( A ) is the prior probability of event A. P ( B ) is the prior probability of event B .

5. -Historical Context & Applications

6. Historical Context The Naïve Bayes algorithm has its roots in the work of Reverend Thomas Bayes, an 18th-century statistician and theologian. However, the algorithm itself, as well as its application to machine learning, gained prominence much later in the 20th century. Here's a brief historical context :

7. Applications of Naive Bayes Text Classification. Sentiment analysis. Recommendation system. Spam filtering. Face Recognition. Weather Prediction. Medical Diagnosis.

8. Applications of Naive Bayes

9. Naïve Assumption:

10. Naïve Assumption: The Naïve Bayes algorithm has its roots in the work of Reverend Thomas Bayes, an 18th-century statistician and theologian. However, the algorithm itself, as well as its application to machine learning, gained prominence much later in the 20th century. Here's a brief historical context :

11. *Advantages and Disadvantages:*

12. **Advantages:** ** Simplicity:** Easy to implement and understand . **Efficiency:** Fast training and prediction times . ** Robustness:** Works well with small datasets and irrelevant features.

13. **Disadvantages :** :** ** Assumption of Independence:** Independence assumption may not hold in real-world scenarios . ** Limited Expressiveness:** May not capture complex relationships in data.

14. *Types of Naïve Bayes Models:*

15. **Gaussian, Multinomial, Bernoulli, and Complementary Naïve Bayes :**

16. THANK YOU