5 research outputs found
Estimating Conditional Distributions with Neural Networks using R package deeptrafo
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
Deep interpretable ensembles
Ensembles improve prediction performance and allow uncertainty quantification
by aggregating predictions from multiple models. In deep ensembling, the
individual models are usually black box neural networks, or recently, partially
interpretable semi-structured deep transformation models. However,
interpretability of the ensemble members is generally lost upon aggregation.
This is a crucial drawback of deep ensembles in high-stake decision fields, in
which interpretable models are desired. We propose a novel transformation
ensemble which aggregates probabilistic predictions with the guarantee to
preserve interpretability and yield uniformly better predictions than the
ensemble members on average. Transformation ensembles are tailored towards
interpretable deep transformation models but are applicable to a wider range of
probabilistic neural networks. In experiments on several publicly available
data sets, we demonstrate that transformation ensembles perform on par with
classical deep ensembles in terms of prediction performance, discrimination,
and calibration. In addition, we demonstrate how transformation ensembles
quantify both aleatoric and epistemic uncertainty, and produce minimax optimal
predictions under certain conditions.Comment: 22 pages main text, 8 figure