!"#

•$!"%$

%

•&!"

•!"'

•('!"

•)!*!"

•!"'+

•,-

.,-/

•0!"

•12'!.!3

'/

•4!"

•#

•0'

•5!

•!!

•,'

('!"

$'%

•A statistical classifier: performs probabilistic prediction, i.e., predicts

class membership probabilities( that a given tuple belongs to a particular

class)

•Foundation: Based on Bayes’ Theorem given by Thomas Bayes

•Performance: A simple Bayesian classifier, naïve Bayesian classifier,

has comparable performance with decision tree and selected neural

network classifiers.

•Class Conditional Independence : Naïve Bayesian Classifiers assume

that the effect of an attribute value on a given class is independent of the

values of the other attributes. This assumption is called class conditional

independence.

•Incremental: Each training example can incrementally increase/decrease

the probability that a hypothesis is correct — prior knowledge can be

combined with observed data

•Standard: Even when Bayesian methods are computationally intractable,

they can provide a standard of optimal decision making against which

other methods can be measured

•Bayesian Belief Network: are graphical models that allow the

representation of dependencies among subsets of attributes

6

('(

•1X!.7evidence8/!!!+9

•1HhypothesisX!!

•5 49!3!"'

:6;'!9<

•!" P(H|X), !' '

!!X. #.=>:/?!'

X9!!'9+9@

•P(H).prior probability/!!'

–5 X9!!'!3A

•P(X):!'3X !'!.

336;'!<

•P(X|H) .posteriori probability/ !' 3 ! X

'!

–5 B X9!!' :6

('

•B X, posteriori probability of a

hypothesis =, #.=>X/, 3!!9('

•&3!!'9

C!+!DE

•# X ! F !' #.>X/

!!#.+>:/3!!k!

•#!G!'!+9!3'

!"!

)(

)()|(

)|( X

X

XP

HPHP

HP

##### Document information

Uploaded by:
amit mohta

Views: 6211

Downloads :
14

University:
Moradabad Institute of Technology (MIT)

Subject:
Data Mining

Upload date:
03/09/2011