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My code can be accessed at: https://github.gatech.edu/jcarr61/supervised-learning.git There are several required python packages. I have included a virtual environment containing them all: * . mp1/bin/activate This is not required if your environment contains the packages already. The script run_learner.py contains the code. It can be run as follows: * python -m run_learner --learner <learner> --dataset <dataset> --kernel <kernel> Learner can be one of: * decision_tree * neural_network * boosting * svm * knn Dataset can be one of: * pima * digits Kernel can be one of (others are possible but only these appear in my report): * linear * rbf The kernel is only used when --learner is svm. run_learner.py will show the hyperparameter optimization graphs on the screen, followed by the learning/scalability/performance curves. That is all. Thank you! Jacob Carr
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