4 papers
Sparse-Group Boosting with Balanced Selection Frequencies: A Simulation-Based Approach and R Implementation
Fabian Obster, Christian Heumann
This paper introduces a novel framework for reducing variable selection bias by balancing selection frequencies of base-learners in boosting and introduces the sgboost package in R…
Using interpretable boosting algorithms for modeling environmental and agricultural data
Fabian Obster, Christian Heumann, Heidi Bohle +1
We describe how interpretable boosting algorithms based on ridge-regularized generalized linear models can be used to analyze high-dimensional environmental data. We illustrate thi…
Factors other than climate change are currently more important in predicting how well fruit farms are doing financially
Fabian Obster, Heidi Bohle, Paul M. Pechan
Machine learning and statistical modeling methods were used to analyze the impact of climate change on financial wellbeing of fruit farmers in Tunisia and Chile. The analysis was b…
Sparse-group boosting -- Unbiased group and variable selection
Fabian Obster, Christian Heumann
In the presence of grouped covariates, we propose a framework for boosting that allows to enforce sparsity within and between groups. By using component-wise and group-wise gradien…