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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PRESENTATION ON NAÏVE BAYESIAN CLASSIFICATION Ashraf Uddin Sujit Singh Chetanya Pratap Singh South Asian University (Master of Computer Application) http://ashrafsau.blogspot.in/ http://ashrafsau.blogspot.in/ Presented By:
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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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