论文标题
机器学习中的灵活模型组成及其在MLJ中的实现
Flexible model composition in machine learning and its implementation in MLJ
论文作者
论文摘要
描述了一种基于图的协议，称为“学习网络”，将各种机器学习模型组合到元模型中。表明学习网络可以克服在主要的机器学习平台中实现的模型组合的几个局限性。在简单的示例中说明了协议之后，提出了用于指定MLJ框架中实现的学习网络的简明语法。使用语法，可以表明学习网络足够灵活，可以包括Wolpert的模型堆叠，并对基本学习者进行样本外预测。
A graph-based protocol called `learning networks' which combine assorted machine learning models into meta-models is described. Learning networks are shown to overcome several limitations of model composition as implemented in the dominant machine learning platforms. After illustrating the protocol in simple examples, a concise syntax for specifying a learning network, implemented in the MLJ framework, is presented. Using the syntax, it is shown that learning networks are are sufficiently flexible to include Wolpert's model stacking, with out-of-sample predictions for the base learners.
