Presentation on the topic Naive_Bayesian_Classifier

Presentation on the topic Naive_Bayesian_Classifier

• 1.
  Naïve Bayesian Classifier •Your Name | Course | Date
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  Introduction • Naïve Bayesis a probabilistic classifier based on Bayes’ Theorem. • Assumes independence between predictors. • Simple, fast, and effective for classification tasks.
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  Bayes’ Theorem • P(H|X)= [P(X|H) * P(H)] / P(X) • - H: Hypothesis (class) • - X: Data (features) • - P(H): Prior probability of hypothesis • - P(X|H): Likelihood • - P(H|X): Posterior probability
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  Naïve Assumption • -Predictors are independent given the class. • - P(X1, X2, ..., Xn | C) = Π P(Xi | C) • This makes computation simple and scalable.
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  Types of NaïveBayes Classifiers • - Gaussian Naïve Bayes: assumes features follow normal distribution • - Multinomial Naïve Bayes: used for text classification • - Bernoulli Naïve Bayes: for binary features
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  Example • Email Classification: •- Features: presence of words like 'offer', 'buy', 'free' • - Classes: Spam / Not Spam • - Naïve Bayes calculates probability of each class and selects the maximum.
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  Steps in NaïveBayes Classification • 1. Convert dataset into frequency table. • 2. Calculate prior probability for each class. • 3. Calculate likelihood for each attribute. • 4. Use Bayes’ theorem to calculate posterior probability. • 5. Assign class with maximum probability.
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  Advantages • - Easyto implement • - Works well with small datasets • - Handles high-dimensional data • - Effective for text classification
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  Limitations • - Assumesindependence of features (not realistic) • - Poor performance with correlated features • - Requires large dataset for reliable probability estimates
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  Applications • - Spamfiltering • - Sentiment analysis • - Document categorization • - Medical diagnosis
• 11.
  Conclusion • - NaïveBayes = Simple, efficient, widely used classifier • - Based on probability and independence assumption • - Popular in text mining, NLP, and classification tasks
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  References • - Han,J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques • - Mitchell, T. M. (1997). Machine Learning