xgboost and XGBoostLSS
XGBoostLSS extends XGBoost's core gradient boosting implementation to enable distributional predictions by modeling location, scale, and shape parameters, making them complementary tools used together rather than alternatives.
About xgboost
dmlc/xgboost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
XGBoost helps data scientists and machine learning engineers quickly build highly accurate predictive models for classification, regression, and ranking tasks. It takes structured datasets (like spreadsheets or database tables) and outputs a powerful model capable of making predictions. This tool is ideal for professionals who need to develop robust and efficient predictive analytics solutions.
About XGBoostLSS
StatMixedML/XGBoostLSS
An extension of XGBoost to probabilistic modelling
This tool helps data scientists and analysts make more robust predictions by forecasting the full range of possible outcomes, not just a single value. It takes in your dataset with various features and outputs not only a prediction, but also the likelihood of different potential results, including prediction intervals and quantiles. This is ideal for professionals who need to understand the uncertainty and risk associated with their forecasts.
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