Model-based Boosting 2.0 Model-based Boosting 2.0
نویسندگان
چکیده
This is an extended version of the manuscript Torsten Hothorn, Peter Bühlmann, Thomas Kneib, Mattthias Schmid and Benjamin Hofner (2010), Model-based Boosting 2.0. Journal of Machine Learning Research, 11, 2109 – 2113; http://jmlr.csail.mit.edu/papers/v11/hothorn10a.html. We describe version 2.0 of the R add-on package mboost. The package implements boosting for optimizing general risk functions utilizing component-wise (penalized) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data.
منابع مشابه
Model-based Boosting 2.0
We describe version 2.0 of the R add-on package mboost. The package implements boosting for optimizing general risk functions using component-wise (penalized) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data.
متن کاملWeb 2.0 Use and Organizational Innovation: A Knowledge Transfer Enabling Perspective
Over the last several years, a variety of Web 2.0 applications has been widely adopted by individual users and recently has received great attention from organizations. While an increasing number of organizations have started utilizing Web 2.0 applications in hopes of boosting collaboration and driving innovations, only a small number of different theoretical perspectives are available in the l...
متن کاملBoosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages CoxBoost and mboost
Despite the limitations imposed by the proportional hazards assumption, the Cox model is probably the most popular statistical tool used to analyze survival data, thanks to its flexibility and ease of interpretation. For this reason, novel statistical/machine learning techniques are usually adapted to fit it, including boosting, an iterative technique originally developed in the machine learnin...
متن کاملCalibrated Boosting-Forest
Excellent ranking power along with well calibrated probability estimates are needed in many classification tasks. In this paper, we introduce a technique, Calibrated Boosting-Forest1 that captures both. This novel technique is an ensemble of gradient boosting machines that can support both continuous and binary labels. While offering superior ranking power over any individual regression or clas...
متن کاملOutlier Detection by Boosting Regression Trees
A procedure for detecting outliers in regression problems is proposed. It is based on information provided by boosting regression trees. The key idea is to select the most frequently resampled observation along the boosting iterations and reiterate after removing it. The selection criterion is based on Tchebychev’s inequality applied to the maximum over the boosting iterations of ...
متن کامل