73 citations · 185 across the 8 of their papers we have counts for
4 papers · 1 filter
A Comparison of Machine Learning Methods for Data with High-Cardinality Categorical Variables
Fabio Sigrist
High-cardinality categorical variables are variables for which the number of different levels is large relative to the sample size of a data set, or in other words, there are few d…
Latent Gaussian Model Boosting
Fabio Sigrist
Latent Gaussian models and boosting are widely used techniques in statistics and machine learning. Tree-boosting shows excellent prediction accuracy on many data sets, but potentia…
Gaussian Process Boosting
Fabio Sigrist
We introduce a novel way to combine boosting with Gaussian process and mixed effects models. This allows for relaxing, first, the zero or linearity assumption for the prior mean fu…
KTBoost: Combined Kernel and Tree Boosting
Fabio Sigrist
We introduce a novel boosting algorithm called `KTBoost' which combines kernel boosting and tree boosting. In each boosting iteration, the algorithm adds either a regression tree o…