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Note for Probability - P by Shiva Prasad

  • Probability - P
  • Note
  • Visvesvaraya Technological University Regional Center - VTU
  • Electronics and Communication Engineering
  • B.Tech
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  • Uploaded 1 year ago
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OUTLINE  Introduction to Bayesian Classification Bayes Theorem  Naïve Bayes Classifier  Classification Example  Classification – an Application  Comparison with other classifiers Advantages and disadvantages  Conclusions  http://ashrafsau.blogspot.in/  Text

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CLASSIFICATION  Classification:  predicts categorical class labels classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and uses it in classifying new data  Typical     Applications credit approval target marketing medical diagnosis treatment effectiveness analysis http://ashrafsau.blogspot.in/ 

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A TWO STEP PROCESS  Model construction: describing a set of predetermined classes  Model usage: for classifying future or unknown objects  Estimate accuracy of the model  The known label of test sample is compared with the classified result from the model  Accuracy rate is the percentage of test set samples that are correctly classified by the model  Test set is independent of training set, otherwise over-fitting will occur http://ashrafsau.blogspot.in/ Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute  The set of tuples used for model construction: training set  The model is represented as classification rules, decision trees, or mathematical formulae 

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INTRODUCTION TO BAYESIAN CLASSIFICATION  What is it ? Statistical method for classification.  Supervised Learning Method.  Assumes an underlying probabilistic model, the Bayes theorem.  Can solve problems involving both categorical and continuous valued attributes.  Named after Thomas Bayes, who proposed the Bayes Theorem. http://ashrafsau.blogspot.in/ 

Lecture Notes