Data scientists seeking a good supervised learning model on a new dataset have many choices to make: they must preprocess the data, select features, possibly reduce the dimension, select an estimation algorithm, and choose hyperparameters for each of these pipeline components. With new pipeline components comes a combinatorial explosion in the number of choices! In this work, we design a new AutoML system to address this challenge: an automated system to design a supervised learning pipeline. Our system uses matrix and tensor factorization as surrogate models to model the combinatorial pipeline search space. Under these models, we develop greedy experiment design protocols to efficiently gather information about a new dataset. Experiments on large corpora of real-world classification problems demonstrate the effectiveness of our approach.
翻译:在新的数据集中寻找良好监督学习模式的数据科学家有许多选择:他们必须预处理数据,选择特征,可能降低尺寸,选择估计算法,并为这些管道的每个部件选择超参数。随着新的管道组件在选择数量上出现组合爆炸!我们在此工作中设计一个新的自动ML系统来应对这一挑战:设计受监督的学习管道的自动化系统。我们的系统使用矩阵和电压因子化作为替代模型来模拟组合管道搜索空间。根据这些模型,我们开发贪婪的实验设计程序来有效收集关于新数据集的信息。对现实世界分类问题大公司进行的实验证明了我们的方法的有效性。