Name
Title | ||
|---|---|---|
Random forest methodology for model-based recursive partitioning: the mobForest package for R. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1186/1471-2105-14-125 | BMC Bioinformatics |
Keywords | Field | DocType |
Random forests, Model-based recursive partitioning, Ensemble, R | Regression,Bootstrapping,Computer science,Software,Recursive partitioning,Sampling (statistics),Bioinformatics,Random forest,Recursion,Partition method | Journal |
Volume | Issue | ISSN |
14 | 1 | 1471-2105 |
Citations | PageRank | References |
6 | 0.37 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nikhil R. Garge | 1 | 43 | 1.48 |
| Georgiy V. Bobashev | 2 | 31 | 4.05 |
| Barry Eggleston | 3 | 6 | 0.37 |