In this assignment you will train a decision tree to predict consumers’ adoption behavior, ‘eReader Adoption

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Decision Trees The data :

In this assignment you will train a decision tree to predict consumers’ adoption behavior, ‘eReader Adoption’, based on their demographic characteristics, e-commerce behaviors, and on other variables such as payment method, The data contains a total of 661 examples and each example is described in terms of 10 attributes including ‘User_ID’. These are in Excel CSV format, with the first line in the file listing the attribute names (separated by commas) and each remaining line in the file giving the values of these attributes for a single consumer. You must train your decision tree classifier for a classification problem.

Tips for data pre/processing: 1. ‘Set Role’ (Process) for the following two attributes in the training dataset ‘DT_Train’: ○1 ‘User_ID’ as ‘ID’, ○2 ‘eReader Adoption’ as ‘Label’.

2. Build a decision tree model to predict “eReader Adoption” (Innovator, Early Adopter, Early Majority, or Late Majority) based on the 9 attributes (excluding User_ID). 3. In Decision Tree, set the parameter for maximal depth to 4. 4. Use ‘Gini_index’ as the selection criterion for a decision tree model.

5. For ‘Split Data’ operator, use the partition ratio of Train (0.8) & Validation (0.2) and the sampling type of stratified sampling. Outputs: 1. RapidMiner Decision Tree description and graph (screenshots) 2. Describe the characteristics of consumers who may become early adopters. 3. Evaluate the performance of your decision tree model in terms of accuracy, recall, and precision (Be specific about the meanings of these 3 criteria). 4. Use gain_ratio in place of gini_index as the selection criteria and compare the performance results. 5. Based on the results of your Decision Tree model, suggest appropriate strategies for each group of consumers.

 

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