Data Mining - Bayesian classification, undefined for Data Mining. Moradabad Institute of Technology (MIT)

Data Mining

Description: Summary about Classification and Prediction, Bayesian Theorem: Basics, Bayesian Theorem, Towards Naïve Bayesian Classifier, Naïve Bayesian Classifier: Training Dataset, Avoiding the 0-Probability Problem.
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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
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Uploaded by: amit mohta
Views: 6269
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Address: Engineering
University: Moradabad Institute of Technology (MIT)
Subject: Data Mining
Upload date: 03/09/2011
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