Fabio SigristinTowards Data ScienceMixed Effects Machine Learning for Longitudinal & Panel Data with GPBoost (Part III)A demo of GPBoost in Python & R using real-world data11 min read·Jul 17, 2023----
Fabio SigristinTowards Data ScienceMixed Effects Machine Learning for High-Cardinality Categorical Variables — Part II: GPBoost…A demo of GPBoost in Python & R using real-world data8 min read·Jul 11, 2023--1--1
Fabio SigristinTowards Data ScienceMixed Effects Machine Learning for High-Cardinality Categorical Variables — Part I: An Empirical…Why random effects are useful for machine learning models10 min read·Jul 7, 2023--1--1
Fabio SigristinTowards Data ScienceMixed Effects Machine Learning with GPBoost for Grouped and Areal Spatial Econometric DataA demo using European GDP data14 min read·Jun 21, 2023--2--2
Fabio SigristinTowards Data ScienceDemystifying ROC and precision-recall curvesDebunking some myths about the ROC curve / AUC and the precision-recall curve / AUPRC for binary classification with a focus on imbalanced…·14 min read·Jan 25, 2022--4--4
Fabio SigristinTowards Data ScienceGeneralized Linear Mixed Effects Models in R and Python with GPBoostAn introduction and comparison with ‘lme4’ and ‘statsmodels’7 min read·Jun 22, 2021--3--3
Fabio SigristinTowards Data ScienceTree-Boosting for Spatial DataGPBoost: Combining Tree-Boosting and Gaussian Process Models13 min read·Mar 15, 2021--1--1
Fabio SigristinTowards Data ScienceTree-Boosted Mixed Effects ModelsGPBoost: Combining Tree-Boosting and Mixed Effects Models11 min read·Aug 12, 2020--7--7
