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.

翻译：统计学习方法近年来日益流行。许多此类方法包含需要调整的参数，以获得性能良好的模型。尽管神经网络领域的研究已十分广泛，但对其他学习方法的关注相对不足。本研究考察了支持向量机、梯度提升机以及AdaBoost在分类和回归场景下的调参行为。通过网格搜索，我们确定了在多种数据集上均能取得良好模型的调参范围。进而探索了不同优化算法，以在调参空间中选择模型。将优化算法选出的模型与网格搜索得到的最优模型进行比较，以甄别性能优异的算法。基于这些发现，我们开发了R语言包EZtune，该工具可自动调整支持向量机与提升树的参数。