Mixed effects machine learning with GPBoost for grouped and areal spatial econometric data

<p>The&nbsp;<a href="https://www.jmlr.org/papers/v23/20-322.html" rel="noopener ugc nofollow" target="_blank">GPBoost algorithm</a>&nbsp;extends linear mixed effects and Gaussian process models by replacing the linear fixed effects function with a non-parametric non-linear function modeled using tree-boosting. This article shows how the GPBoost algorithm implemented in the&nbsp;<code><a href="https://github.com/fabsig/GPBoost" rel="noopener ugc nofollow" target="_blank">GPBoost</a></code><a href="https://github.com/fabsig/GPBoost" rel="noopener ugc nofollow" target="_blank">&nbsp;library</a>&nbsp;can be used for modeling data with a spatial and grouped structure. We demonstrate the functionality of the&nbsp;<code>GPBoost</code>&nbsp;library using European GDP data which is an example of areal spatial econometric data.</p> <p><a href="https://medium.com/towards-data-science/mixed-effects-machine-learning-with-gpboost-for-grouped-and-areal-spatial-econometric-data-b26f8bddd385">Read More</a></p>