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Data MiningExplain Naive Bayesian classification algorithm.20207mData MiningBayesian classifiers is (i) a class of learning algorithm that tries to find an optimum classification of a set of examples using the probabilistic theory (ii) any mechanism employed by a learning system to constrain the search space of a hypothesis (iii) an approach to the design of learning algorithms that is inspired by the fact that when people encounter new situations, they often explain them by reference to familiar experiences, adapting the explanations to fit the new situation (iv) None of the above20202mData MiningDefine Classification and Prediction in data mining. Describe the concept of Bayesian Classification with a simple example.20257mMACHINE LEARNINGWhich of the following is true about Naive Bayes? (i) Assumes that all the features in a dataset are equally important (ii) Assumes that all the features in a dataset are independent (ii) Both (i) and (ii) (iv) None of the above20222m
PreviousA database has five transactions. Let min sup = 60% and min con f = 80% : | TID | items_bought | |---|---| | T100 | {M, O, N, K, E, Y} | | T200 | {D, O, N, K, E, Y} | | T300 | {M, A, K, E} | | T400 | {M, U, C, K, Y} | | T500 | {C, O, O, K, I, E} | Find all frequent item sets using Apriori and FP-growth, respectively. Compare the efficiency of the two mining processes.NextWhat is boosting? State why it may improve the accuracy of decision tree induction.