Breiman and Cutler's Random Forests for Classification and Regression
Prototypes of groups.
Combine Ensembles of Trees
Extract a single tree from a forest.
Add trees to an ensemble
Extract variable importance measure
Margins of randomForest Classifier
Multi-dimensional Scaling Plot of Proximity matrix from randomForest
Rough Imputation of Missing Values
Compute outlying measures
Partial dependence plot
Plot method for randomForest objects
predict method for random forest objects
Classification and Regression with Random Forest
Random Forest Cross-Valdidation for feature selection
Missing Value Imputations by randomForest
Show the NEWS file
Size of trees in an ensemble
Tune randomForest for the optimal mtry parameter
Variable Importance Plot
Variables used in a random forest
Classification and regression based on a forest of trees using random inputs, based on Breiman (2001) <DOI:10.1023/A:1010933404324>.