REVIEW 2 cited by
XGBoostLSS -- An extension of XGBoost to probabilistic forecasting
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We propose a new framework of XGBoost that predicts the entire conditional distribution of a univariate response variable. In particular, XGBoostLSS models all moments of a parametric distribution (i.e., mean, location, scale and shape [LSS]) instead of the conditional mean only. Choosing from a wide range of continuous, discrete and mixed discrete-continuous distribution, modelling and predicting the entire conditional distribution greatly enhances the flexibility of XGBoost, as it allows to gain additional insight into the data generating process, as well as to create probabilistic forecasts from which prediction intervals and quantiles of interest can be derived. We present both a simulation study and real world examples that demonstrate the virtues of our approach.
Forward citations
Cited by 2 Pith papers
-
Parallel gradient boosting for flexible estimation of conditional distributions
A modified gradient-boosting algorithm trains one univariate weak learner per iteration for all output targets, giving similar accuracy to XGBoost for multiple quantile regression while cutting runtime by up to roughly 50x.
-
From Point to probabilistic gradient boosting for claim frequency and severity prediction
A benchmark of ten gradient boosting algorithms on five insurance datasets shows probabilistic versions can improve fit without losing predictive accuracy, with LightGBM and XGBoostLSS fastest.
Discussion (0). Continue with ORCID to comment.