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 dataJul 17, 2023Jul 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 dataJul 11, 20231Jul 11, 20231
Fabio SigristinTowards Data ScienceMixed Effects Machine Learning for High-Cardinality Categorical Variables — Part I: An Empirical…Why random effects are useful for machine learning modelsJul 7, 20231Jul 7, 20231
Fabio SigristinTowards Data ScienceMixed Effects Machine Learning with GPBoost for Grouped and Areal Spatial Econometric DataA demo using European GDP dataJun 21, 20232Jun 21, 20232
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…Jan 25, 20224Jan 25, 20224
Fabio SigristinTowards Data ScienceGeneralized Linear Mixed Effects Models in R and Python with GPBoostAn introduction and comparison with ‘lme4’ and ‘statsmodels’Jun 22, 20213Jun 22, 20213
Fabio SigristinTowards Data ScienceTree-Boosting for Spatial DataGPBoost: Combining Tree-Boosting and Gaussian Process ModelsMar 15, 20211Mar 15, 20211
Fabio SigristinTowards Data ScienceTree-Boosted Mixed Effects ModelsGPBoost: Combining Tree-Boosting and Mixed Effects ModelsAug 12, 20207Aug 12, 20207