Random forest methodology for model-based recursive partitioning: the mobForest package for R.
Random forest methodology for model-based recursive partitioning: the mobForest package for R.
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基于模型的递归分区的随机森林方法：R。
DOI：
10.1186/1471-2105-14-125
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发表时间：
2013-04-11
影响因子：
3
通讯作者：
Eggleston B
中科院分区：
文献类型：
--
作者：
Garge NR;Bobashev G;Eggleston B
递归划分是一种非参数建模技术，广泛应用于回归和分类问题。基于模型的递归划分用于识别具有相似感兴趣模型参数值的观察组。R中PARTY包中的mob()函数实现了基于模型的递归分区方法。该方法基于单树模型生成预测。通过单树模型获得的预测对学习样本的微小变化非常敏感。我们扩展了基于模型的递归划分方法，以生成基于随机样本的多树模型，所述随机样本通过对学习数据进行自举(带替换的随机采样)或次采样(不带替换的随机采样)来实现。在这里，我们展示了一个名为“mobForest”的R包，它为基于模型的递归分区实现了袋装和随机森林方法。MobForest包构建了大量基于模型的树，并在这些树上聚合预测，从而产生更稳定的预测。该软件包还包括计算预测精度估计和曲线图、残差曲线图和可变重要性曲线图的功能。MobForest包实现了一种用于基于模型的递归分区的随机森林类型方法。R包及其源代码可在http://CRAN.R-project.org/package=mobForest.上获得
Recursive partitioning is a non-parametric modeling technique, widely used in regression and classification problems. Model-based recursive partitioning is used to identify groups of observations with similar values of parameters of the model of interest. The mob() function in the party package in R implements model-based recursive partitioning method. This method produces predictions based on single tree models. Predictions obtained through single tree models are very sensitive to small changes to the learning sample. We extend the model-based recursive partition method to produce predictions based on multiple tree models constructed on random samples achieved either through bootstrapping (random sampling with replacement) or subsampling (random sampling without replacement) on learning data. Here we present an R package called “mobForest” that implements bagging and random forests methodology for model-based recursive partitioning. The mobForest package constructs large number of model-based trees and the predictions are aggregated across these trees resulting in more stable predictions. The package also includes functions for computing predictive accuracy estimates and plots, residuals plot, and variable importance plot. The mobForest package implements a random forest type approach for model-based recursive partitioning. The R package along with it source code is available at http://CRAN.R-project.org/package=mobForest.
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影响因子：
7.5
作者：
Dietterich, TG
通讯作者：
Dietterich, TG
影响因子：
7.5
作者：
Bauer, E;Kohavi, R
通讯作者：
Kohavi, R
影响因子：
7.5
作者：
Breiman, L
通讯作者：
Breiman, L
影响因子：
7
作者：
Strobl, Carolin;Malley, James;Tutz, Gerhard
通讯作者：
Tutz, Gerhard
影响因子：
30.8
作者：
Blewitt, Marnie E.;Gendrel, Anne-Valerie;Whitelaw, Emma
通讯作者：
Whitelaw, Emma