To build classifiers ex) Naive Bayes, Logistic Regression

1. Generative Classifiers(Naive Bayes): build a model of how a class could generate some input data.

Given an observation, they return the class most likely to have generated the obsevation.

2. Discriminative Classifiers(Logistic Regression): learn what features from the input are most useful to discriminate between the different possible classes.

Naive Bayes Classifiers

multinomial naive Bayes classifier

• present a test document as bag-of-words(an unordered set of words with their position ignored, keeping only their "frequency" in the document)

• probablistic classifier

• out of all classes c the classifier returns the class which has the maximum posterior probability(조건부 확률) given the document

Classification

A mapping h from input data x(drawn from instance space X) to a label(or labels) y from some enumerable output space Y