  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.
