Contemporary empirical applications frequently require flexible regression models for complex response types and large tabular or non-tabular, including image or text, data. Classical regression models either break down under the computational load of processing such data or require additional manual feature extraction to make these problems tractable. Here, we present deeptrafo, a package for fitting flexible regression models for conditional distributions using a tensorflow backend with numerous additional processors, such as neural networks, penalties, and smoothing splines. Package deeptrafo implements deep conditional transformation models (DCTMs) for binary, ordinal, count, survival, continuous, and time series responses, potentially with uninformative censoring. Unlike other available methods, DCTMs do not assume a parametric family of distributions for the response. Further, the data analyst may trade off interpretability and flexibility by supplying custom neural network architectures and smoothers for each term in an intuitive formula interface. We demonstrate how to set up, fit, and work with DCTMs for several response types. We further showcase how to construct ensembles of these models, evaluate models using inbuilt cross-validation, and use other convenience functions for DCTMs in several applications. Lastly, we discuss DCTMs in light of other approaches to regression with non-tabular data.
翻译:现代经验应用经常要求复杂响应类型和大型表表格或非表单的灵活回归模型,包括图像或文本、数据。典型回归模型要么在处理这些数据的计算负荷下分解,要么需要额外的手工特征提取,使这些问题可以处理。这里,我们提出“深轨”软件,这是一个套件,用于安装弹性回归模型,用于使用一个加装无数其他处理器(如神经网络、惩罚和滑滑动样条)的发配条件条件条件,用于复杂的响应类型,包括图象或文字、数据、典型回归模型(DCTMs),用于二进制、正态、计数、生存、连续和时间序列响应,可能采用不提供信息性审查。与其他可用方法不同,DCTMs不承担一个参数分布的准分类。此外,数据分析员可以通过在直观的公式界面中为每个术语提供定制的神经网络架构和滑动器来交换可解释性和灵活性。我们演示如何为几种响应类型安装、适应和配合DCTMs。我们进一步展示如何构建这些模型的组合,用其他不透明的方法来评估这些模型,在建制式的系统中,用不便利式的模型来讨论其他的模型,在DTMsrque压中,我们使用其他数据。