9 citations · 22 across the 8 of their papers we have counts for
5 papers · 1 filter
Churn modeling of life insurance policies via statistical and machine learning methods -- Analysis of important features
Andreas Groll, Carsten Wasserfuhr, Leonid Zeldin
Life assurance companies typically possess a wealth of data covering multiple systems and databases. These data are often used for analyzing the past and for describing the present…
Machine Learning for Multi-Output Regression: When should a holistic multivariate approach be preferred over separate univariate ones?
Lena Schmid, Alexander Gerharz, Andreas Groll +1
Tree-based ensembles such as the Random Forest are modern classics among statistical learning methods. In particular, they are used for predicting univariate responses. In case of…
Deducing neighborhoods of classes from a fitted model
Alexander Gerharz, Andreas Groll, Gunther Schauberger
In todays world the request for very complex models for huge data sets is rising steadily. The problem with these models is that by raising the complexity of the models, it gets mu…
Random boosting and random^2 forests -- A random tree depth injection approach
Tobias Markus Krabel, Thi Ngoc Tien Tran, Andreas Groll +2
The induction of additional randomness in parallel and sequential ensemble methods has proven to be worthwhile in many aspects. In this manuscript, we propose and examine a novel r…
Hybrid Machine Learning Forecasts for the FIFA Women's World Cup 2019
Andreas Groll, Christophe Ley, Gunther Schauberger +2
In this work, we combine two different ranking methods together with several other predictors in a joint random forest approach for the scores of soccer matches. The first ranking…