A Fast Implementation of Random Forests
Case-specific random forests.
Deforesting a random forest
Get terminal node IDs (deprecated)
Hold-out random forests
ranger variable importance
ranger variable importance p-values
Parse formula
Ranger prediction
Ranger prediction
Ranger predictions
Ranger predictions
Print deforested ranger summary
Print Ranger forest
Print Ranger prediction
Print Ranger
Ranger
Ranger timepoints
Ranger timepoints
Tree information in human readable format
A fast implementation of Random Forests, particularly suited for high dimensional data. Ensembles of classification, regression, survival and probability prediction trees are supported. Data from genome-wide association studies can be analyzed efficiently. In addition to data frames, datasets of class 'gwaa.data' (R package 'GenABEL') and 'dgCMatrix' (R package 'Matrix') can be directly analyzed.
Useful links