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== Running SVM on the Data == * cd into your libsvm installation's tools directory and run the following command (assuming your training and test files are in ~/kdd and named appropriately): python easy.py ~/kdd/algebra_2008_2009_train.txt_sample_10_random_students.csv_converted.txt ~/kdd/algebra_2008_2009_train.txt_sample_10_random_students.csv_converted.t | tee output.txt * If you have many predictor variables, this will take a long time. Prohibitively long, probably. * This will automatically scale your training and test data and iteratively search over the parameter space for penalty parameter c and kernel parameters, using cross-validation in order to find the best fit for the training data. * It will generate an output for each item in the test set with a 0/1 classification. Apparently there is a way to get libSVM to output real values between 0 and 1 (depending on confidence), but we haven't yet investigated doing this.
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