Tuning support vector machines and boosted trees using optimization algorithms
This work addresses the need for efficient parameter tuning in statistical learning methods like SVMs and boosted trees, though it is incremental as it builds on existing grid search and optimization techniques.
The authors tackled the problem of tuning parameters for support vector machines and boosted trees by using optimization algorithms to select models, resulting in the creation of an R package called EZtune for automatic tuning.
Statistical learning methods have been growing in popularity in recent years. Many of these procedures have parameters that must be tuned for models to perform well. Research has been extensive in neural networks, but not for many other learning methods. We looked at the behavior of tuning parameters for support vector machines, gradient boosting machines, and adaboost in both a classification and regression setting. We used grid search to identify ranges of tuning parameters where good models can be found across many different datasets. We then explored different optimization algorithms to select a model across the tuning parameter space. Models selected by the optimization algorithm were compared to the best models obtained through grid search to select well performing algorithms. This information was used to create an R package, EZtune, that automatically tunes support vector machines and boosted trees.