decisiontree's Introduction
Environment details ------------------- Language Java(1.6) Instructions ------------ compiling: javac decisionTree.java Generic Run format: java decisionTree <L> <K> <training_set.csv> <validation_set> <test_set.csv> <to-print> Sample test cases: java decisionTree 100 5 ds1_training_set.csv ds1_validation_set.csv ds1_test_set.csv no java decisionTree 200 10 ds1_training_set.csv ds1_validation_set.csv ds1_test_set.csv yes java decisionTree 100 5 ds2_training_set.csv ds2_validation_set.csv ds2_test_set.csv yes java decisionTree 200 10 ds2_training_set.csv ds2_validation_set.csv ds2_test_set.csv no if <to-print> option is set, decision tree will be written to the standard output Approach -------- 1.Tree is created with <training_set.csv> using Information Gain Heuristic 2.Pruning is done using <validation_set.csv> 3.Accuracy is measured on <test_set.csv> note: accuracy is measure of (correctly predicted instances)/(total instances)
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