Page 100 - Demo

%u062c%u0645%u064a%u0639 %u0627%u0644%u062d%u0642%u0648%u0642 %u0645%u062d%u0641%u0648%u0638%u0629 %u0640 %u0627%u0625%u0644%u0639%u062a%u062f%u0627%u0621 %u0639%u0649%u0644 %u062d%u0642 %u0627%u0645%u0644%u0624%u0644%u0641 %u0628%u0627%u0644%u0646%u0633%u062e %u0623%u0648 %u0627%u0644%u0637%u0628%u0627%u0639%u0629 %u064a%u0639%u0631%u0636 %u0641%u0627%u0639%u0644%u0647 %u0644%u0644%u0645%u0633%u0627%u0626%u0644%u0629 %u0627%u0644%u0642%u0627%u0646%u0648%u0646%u064a%u0629100common approach is postpruning, which removes subtrees from a %u201cfully grown%u201d tree. A subtree at a given node is pruned by removing its branches and replacing it with a leaf. The leaf is labeled with the most frequent class among the subtree being replaced. III.Bayes Classification Methods Bayesian classifiers are statistical classifiers. They can predict class membership probabilities such as the probability that a given tuple belongs to a particular class. Why Bayesian Classification? A statistical classifier performs probabilistic prediction, i.e., predicts class membership probabilities.its foundation is based on Bayes%u2019 Theorem. Its performance: A simple Bayesian classifier, na%u00efve Bayesian classifier, has comparable performance with decision tree and selected neural network classifiers. each training example can incrementally increase/decrease the probability that a hypothesis is correct %u2014 prior knowledge can be combined with observed data. Even when Bayesian methods are computationally intractable, they can provide a standard of optimal decision making against which other methods can be measured. Bayes%u2019 Theorem: Let X be a data tuple. In Bayesian terms, X is considered %u201cevidence.%u201d As usual, it is described by measurements made on a set of n attributes. Let H be some hypothesis such as that the data tuple X belongs to a specified class C. For classification problems, we want to determine P(H|X), the probability that the hypothesis H holds given the %u201cevidence%u201d or observed data tuple X. In other words, we are looking for the probability that tuple X belongs to class C, given that we know the attribute description of X. P(H |X)=P(X|H)P(H) =P(X|H) P(H)/P(X)P(X)