Naive Bayes Classifier Presentaion.pptx

A group of classification algorithms built using the Bayes Theorem are known as Naive Bayes classifiers. It is a family of algorithms rather than a single method...

1. Presentation By Syed Mohamamed Rehan Manoj Kumar C S Sam Sagaya Raj Sanju K N Naive Bayes Classifier

2. Contents Introduction Conditional Probability Bayes theorem Objective Uses

3. A group of classification algorithms built using the Bayes Theorem are known as Naive Bayes classifiers. It is a family of algorithms rather than a single method A mathematical procedure called Bayes can be used to calculate conditional probability. The probability of a result happening based on the possibilities that has a comparable outcome which has already happened in the past is known as conditional probability. Introduction

4. The Bayes theorem offers a procedure to update probabilities for the current predictions or theories given new or additional evidence. I t can be used for large data sets Naive Bayes extremely fast relative to other classification algorithms. Introduction

5. Conditional p robability formula : P(B|A) = P(A and B) / P(A) OR P(B|A) = P(A∩B) / P(A) Where, P = Probability A = Event A B = Event B Conditional Probability

6. P(A | B) = (P( B | A )P(A ))/P( B) where A and B are events. P (A|B) is called the posterior probabilities . P (B| A) is called the likelihood probabilities. P(A) is known as the Marginal probabilities. P (B) is known as the prior probabilities. Bayes Theorem

7. Objectives The proposed approach is a supervised approach. Here we are finding the Iris flower species based on flower attribute measurements. Four features were measured from each sample are as follows:  Sepal length  Sepal width  Petal length Petal width Species of Iris: Iris Setosa Iris Versicolor Iris Virginica Iris Setosa Iris Versicolor Iris Virginica

8. Document classification. This algorithm can help you to determine to which category a given document belongs. It can be used to classify texts into different languages, genres, or topics (through the presence of keywords). Spam filtering. Naive Bayes easily sorts out spam using keywords. For example, in spam, you can see the word ‘100% free’ much more often than in regular mail. The algorithm must be trained to recognize such probabilities and, then, it can efficiently apply them for spam filtering. Uses

9. Sentiment analysis. Based on what emotions the words in a text express, Naive Bayes can calculate the probability of it being positive or negative. For example, in customer reviews, ‘good’ or ‘inexpensive’ usually mean that the customer is satisfied. However, Naive Bayes is not sensitive to sarcasm. Uses

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