(f)RFCDE: Random Forests for Conditional Density Estimation and Functional Data

By Taylor Pospisil, Ann B. Lee. 2019

11 citations recorded by the source corpus.

Random forests is a common non-parametric regression technique which performs well for mixed-type unordered data and irrelevant features, while being robust to monotonic variable transformations. Standard random forests, however, do not efficiently handle functional data and runs into a curse-of dimensionality when presented with high-resolution curves and surfaces. Furthermore, in settings with heteroskedasticity or multimodality, a regression point estimate with standard errors do not fully capture the uncertainty in our predictions. A more informative quantity is the conditional density p(y | x) which describes the full extent of the uncertainty in the response y given covariates x. In this paper we show how random forests can be efficiently leveraged for conditional density estimation, functional covariates, and multiple responses without increasing computational complexity. We provide open-source software for all procedures with R and Python versions that call a common C++ library.

Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

Mathematical optimization in classification and regression trees (2021, background): “2019), functional data (Balakrishnan and Madigan 2006; Möller et al. 2016; Pospisil and Lee 2019; Rahman et al. 2019), text data (Martens and Provost minimize ‖ ‖”