R&D of advanced gradient boosting algorithms implemented in artificial neural-networks, as a form of Time-Series execution.
XGBoost is a software library that stands for extreme gradient boosting. The library is an implementation of gradient boosting machines created by Tianqi Chen as a research project, and since contributed to and by other developers.
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XGBoost focuses on computational speed and model performance, offering optimization features such as Gradient Boosting, Stochastic Gradient Boosting, and Regularized Gradient Boosting.
These repositories are a (WIP) work in progress, and not fully-functional without workflow/actions setup and OAuth.
The XGBoost algorithm is implemented to maximize the efficiency of compute time and memory resources. The algorithm features Sparse Aware implementation by automatically handling missing values from data sets. Additionally, the algorithm allows for continued training of an already fitted model on new data.
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.
It initiates machine learning algorithms under the Gradient Boosting framework.
XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.
The same code runs on major distributed environment (Kubernetes, Hadoop, SGE, MPI, Dask) and can solve problems beyond billions of examples.
XGBoost is a popular open source software library due mainly to the fact that it is really fast. Compared to other methods of gradient boosting, The Algorithm consistently performs faster than implementations from Python, Spark, and R.
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