EZtune: Tunes AdaBoost, Support Vector Machines, and Gradient Boosting Machines

Contains two functions that are intended to make tuning supervised learning methods easy. The eztune function uses a genetic algorithm or quasi-Newton-Raphson optimizer to find the best set of tuning parameters. The user can choose the optimizer, the learning method, and if optimization will be based on accuracy obtained through resubstitution or cross-validation. The function eztune.cv will compute a cross-validated error rate. The purpose of eztune.cv is to provide a cross-validated accuracy when resubstitution is used for optimization because resubstitution typically produces an accuracy that is too high.

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