English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We propose a new framework of XGBoost that predicts the entire conditionaldistribution of a univariate response variable. In particular, XGBoostLSSmodels all moments of a parametric distribution (i.e., mean, location, scaleand shape [LSS]) instead of the conditional mean only. Choosing from a widerange of continuous, discrete and mixed discrete-continuous distribution,modelling and predicting the entire conditional distribution greatly enhancesthe flexibility of XGBoost, as it allows to gain additional insight into thedata generating process, as well as to create probabilistic forecasts fromwhich prediction intervals and quantiles of interest can be derived. We presentboth a simulation study and real world examples that demonstrate the virtues ofour approach.

XGBoostLSS -- An extension of XGBoost to probabilistic forecasting