Naive Bayes is the most straight forward and fast classification algorithm, which is suitable for large chunk of data.Naive Bayes is a statistical classification technique based on Bayes theorem. It is one of the simplest supervised learning algorithms.
Applications :
- Spam filtering
- Text classification
- Sentiment Analysis
- Recommended systems and etc,.
Features :
- Naive Bayes classifier is fast, accurate and reliable algorithm
- Naive Bayes classifiers have high accuracy and speed on large data sets.
For better understanding this classifier, we must know some basic probability concepts like Bayes theorem and conditional probability.
For instance, from the deck of cards pick a random card, what is the probability of getting a queen??
we know that, total number of cards = 52, and out of 4 are Queen.
- P(Queen) = 4/52 = 1/13.
Naive Bayes classifier assumes that the effect of a particular feature in a class is independent of other features.
- P( h ) : The probability of hypothesis h being true (regardless of the data).This is known as prior probability of h
- P(D) : The probability of data ( regardless of the hypothesis), known as prior probability.
- P(h/D) : The probability of hypothesis h given the data D, known as Posterior probability.
- P(D/h) : The probability of data D given that the hypothesis h was true. This is known as Posterior Probability.
Types of Naive Bayes classifier:
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