CatBoostLSS
An extension of CatBoost to probabilistic modelling
We propose a new framework of [CatBoost](https://github.com/catboost/catboost) that predicts the entire conditional distribution of a univariate response variable. In particular, **CatBoostLSS** 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 distributions, modelling and predicting the entire conditional distribution greatly enhances... The project is distributed under the MIT License license, first published in 2019. Key topics include: catboost, distributional-regression, gamlss, machine-learning, prediction-intervals.
CatBoostLSS - An extension of CatBoost to probabilistic modelling
We propose a new framework of CatBoost that predicts the entire conditional distribution of a univariate response variable. In particular, CatBoostLSS 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 distributions, modelling and predicting the entire conditional distribution greatly enhances the flexibility of CatBoost, 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. In the following, we provide a short walk-through of the functionality of CatBoostLSS.
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