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2nd May 2011 LAB 9: Data Mining Technique: Classification Statement Purpose: Today we will continue the topic from the last week which is Data Mining Techniques. As you already knew that we use data mining for providing intelligence to our business by extracting patterns and new rules from the previous data. Today the data mining technique which we will see is Classification Decision Tree & Naïve Bayes Algorithm. Activity Outcomes: The main purpose of this lab is to prepare student for using classification technique by using Rapid Miner. Including, building, testing and analyzing the classification models using decision tree and Naïve Bayes algorithm. Instructor Note: Follow the instructions. CPIS-342 - The Lab Note Lab 9 2nd May 2011 LAB 9: Data Mining Technique: Classification Today we use sample data set from Rapid Miner Repositories Open rapid miner and go to Repositories. Go to Samples then Data use the Golf data set Here we will use Decision tree method CPIS-342 - The Lab Note Lab 9 2nd May 2011 LAB 9: Data Mining Technique: Classification First, using Decision Tree In Operators expand Modeling ,in classification and regression expand Tree Induction ,use DecisionTree Observes the result CPIS-342 - The Lab Note Lab 9 2nd May 2011 LAB 9: Data Mining Technique: Classification Second, using Bayesian Modeling Import the Golf data set In Operators expand Modeling, in classification and regression expand Bayesian Modeling, use Naïve Bayes CPIS-342 - The Lab Note Lab 9 2nd May 2011 LAB 9: Data Mining Technique: Classification Observes the result CPIS-342 - The Lab Note Lab 9 2nd May 2011 LAB 9: Data Mining Technique: Classification Some Theory about Decision Tree and Naïve Bayes algorithm for more understanding: Decision tree: used in statistics, data mining and machine learning, uses a decision tree as a predictive model which maps observations about an item to conclusions about the item's target value. More descriptive names for such tree models are classification trees or regression trees. In these tree structures, leaves represent classifications and branches represent conjunctions of features that lead to those classifications. A tree showing survival of passengers on the Titanic ("sibsp" is the number of spouses or siblings aboard). The figures under the leaves show the probability of survival and the percentage of observations in the leaf. A Naive Bayes classifier : is a simple probabilistic classifier based on applying Bayes' theorem (from Bayesian statistics) with strong (naive) independence assumptions. A more descriptive term for the underlying probability model would be "independent feature model". In simple terms, a naive Bayes classifier assumes that the presence (or absence) of a particular feature of a class is unrelated to the presence (or absence) of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 4" in diameter. Even if these features depend on each other or upon the existence of the other features, a naive Bayes classifier considers all of these properties to independently contribute to the probability that this fruit is an apple. CPIS-342 - The Lab Note Lab 9