Model-based Boosting 2.0

By Torsten Hothorn, Peter Buhlmann, Thomas Kneib, Matthias Schmid, B. Hofner. 2010

201 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

Causal inference by using invariant prediction: identification and confidence intervals (2015, methodology): “We use L 2 -boosting [Friedman, 2001, B¨uhlmann and Yu, 2003] from the R-package mboost [Hothorn et al., 2010] with shrinkage 0 .”
CAM: Causal Additive Models, high-dimensional order search and penalized regression (2013, methodology): “We implement this using the R-function gamboost from the package mboost (Hothorn et al., 2010).”
A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, methodology): “Lately, there have been several alterations of boosting algorithms [92], which include likelihood-based boosting [93], model-based boosting [94], GAMBoost [95], CoxBoost [96], and CatBoost [97].”
Fast Calibrated Additive Quantile Regression (2017, methodology): “We also include quantile regression by gradient boosting, as implemented in the mboost R package (Hothorn et al., 2010).”
Gradient boosting machines, a tutorial (2013, background): “The simplest form of regularization through shrinkage is the direct proportional shrinkage (Friedman, 2001; Hothorn et al., 2010).”
Microbial Co-occurrence Relationships in the Human Microbiome (2012, methodology): “This tuning parameter and the model fitting process was 10-fold cross-validated and selected from the most accurate (by root mean square error for continuous tt and AUC for binary) of 50, 100, or 150 boosting iterations using the caret [75] and mboost [76] R packages.”