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Compare the advantages and disadvantages of eager classification (e.g., decision tree, Bayesian, neural network) versus lazy classification (e.g., k-nearest neighbor, case-based reasoning).  ( 1 Mark)

 

Q4. The following decision tree has been created to predict what someone can do. co

 

IT446DATA MINING & DATA WAREHOUSING26.11.2016Assignment #3Due Date: Saturday 3rdDec 11:59 PMTotal Marks: 4Q1.Given a decision tree, you have the option of (a)convertingthe decision tree to rules andthen pruning the resulting rules, or (b)pruningthe decision tree and then converting the prunedtreetorules.Whatadvantagedoes(a)haveover(b)?(0.75 Mark)Q2.See the following Figure and compute the true positive rate .TPR/ , false positive rate .FPR/,Precision and Accuracy.(2 Marks)ï‚·There are two possible predicted classes: "yes" and "no". If we predict the presence of adisease, for example, "yes" would mean they have the disease, and "no" would mean theydon't have the disease.ï‚·The classifier made a total of 165 predictions (e.g., 165 patients were being tested for thepresence of that disease).ï‚·Out of those 165 cases, the classifier predicted "yes" 110 times, and "no" 55 times.ï‚·In reality, 105 patients in the sample have the disease, and 60 patients do not.Q3.Compare the advantages and disadvantages ofeagerclassification (e.g., decision tree,Bayesian, neural network) versuslazyclassification (e.g.,k-nearest neighbor, case-basedreasoning).( 1 Mark)

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