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Predictive Distribution Modeling Using Transformation Forests

Regression models for supervised learning problems with a continuous response are commonly understood as models for the conditional mean of the response given predictors. This notion is simple and therefore appealing for interpretation and visualization. Information about the whole underlying conditional distribution is, however, not available from these models. A more general understanding of regression models as models for conditional distributions allows much broader inference, for example, the computation of prediction intervals or probabilistic predictions for exceeding certain thresholds. Several random forest-type algorithms aim at estimating conditional distributions, most prominently quantile regression forests. We propose a novel approach based on a parametric family of distributions characterized by their transformation function. A dedicated novel “transformation tree” algorithm able to detect distributional changes is developed. Based on these transformation trees, we introduce “transformation forests” as an adaptive local likelihood estimator of conditional distribution functions. The resulting predictive distributions are fully parametric yet very general and allow inference procedures, such as likelihood-based variable importances, to be applied in a straightforward way. Supplemental files for this article are available online.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS; Vol.30 No.4 December 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1181-1196

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

RANDOM FOREST
CONDITIONAL DISTRIBUTION
TRANSFORMATION MODEL
CONDITIONAL QUANTILES
QUANTILE REGRESSION FOREST

Info Detail Spesifik

https://doi.org/10.1080/10618600.2021.1872581

Pernyataan Tanggungjawab

Torsten Hothorn, Achim Zeileis

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