Hyperparameter optimization is the process of identifying the appropriate hyperparameter configuration of a given machine learning model with regard to a given learning task. For smaller data sets, an exhaustive search is possible; However, when the data size and model complexity increase, the number of configuration evaluations becomes the main computational bottleneck. A promising paradigm for tackling this type of problem is surrogate-based optimization. The main idea underlying this paradigm considers an incrementally updated model of the relation between the hyperparameter space and the output (target) space; the data for this model are obtained by evaluating the main learning engine, which is, for example, a factorization machine-based model. By learning to approximate the hyperparameter-target relation, the surrogate (machine learning) model can be used to score large amounts of hyperparameter configurations, exploring parts of the configuration space beyond the reach of direct machine learning engine evaluation. Commonly, a surrogate is selected prior to optimization initialization and remains the same during the search. We investigated whether dynamic switching of surrogates during the optimization itself is a sensible idea of practical relevance for selecting the most appropriate factorization machine-based models for large-scale online recommendation. We conducted benchmarks on data sets containing hundreds of millions of instances against established baselines such as Random Forest- and Gaussian process-based surrogates. The results indicate that surrogate switching can offer good performance while considering fewer learning engine evaluations.
翻译:超光度优化是确定特定机器学习模型与特定学习任务的适当超光度配置的过程。对于较小的数据集来说,可以进行彻底搜索; 但是,当数据大小和模型复杂性增加时,配置评价的数量成为主要的计算瓶颈。处理这类问题的有希望的范例是代用优化。这一模式的主要理念是考虑超光度空间与输出(目标)空间之间关系的逐步更新模式;这一模型的数据是通过评价主要学习引擎获得的,例如,一种因子化机基模型。通过学习近似超光度目标关系,可使用超光度(机械学习)模型来评分大量超光度配置,探索超出直接机器学习引擎评估范围的部分配置空间。通常,在优化初始化之前选择一个代孕模型,在搜索期间保持相同状态。我们调查了在优化期间对代孕模型进行动态转换是否是选择最适当的因子化机械化模型的合宜性点,通过学习超焦距目标关系,可以使用超光度配置模型,探索超出直接机器学习引擎引擎引擎引擎评估范围的部分。我们用以百万个基模型进行模拟模拟模拟模拟模拟模拟测试,同时进行模拟模拟模拟模拟模拟模拟测试。我们以模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟测试,以模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟模拟测试。