Man pages for neurodata/R-RerF
Randomer Forest

BuildTreeRerF Tree Generator
checkInputMatrixDetermine if given input can be processed by Urerf.
ComputeSimilarityCompute Similarities
defaultsDefault values passed to RandMat*
FeatureImportanceCompute Feature Importance of a RerF model
GrowUnsupervisedForestCreates Urerf Tree.
makeABCreate rotation matrix used to determine mtry features.
mnistA subset of the MNIST dataset for handwritten digit...
OOBPredictCompute out-of-bag predictions
PackForestPacks a forest and saves modified forest to disk for use by...
PackPredictCompute class predictions for each observation in X
PredictCompute class predictions for each observation in X
RandMatBinaryCreate a Random Matrix: Binary
RandMatContinuousCreate a Random Matrix: Continuous
RandMatCustomCreate a Random Matrix: custom
RandMatFRCCreate a Random Matrix: FRC
RandMatFRCNCreate a Random Matrix: FRCN
RandMatImageControlCreate a Random Matrix: image-control
RandMatImagePatchCreate a Random Matrix: image-patch
RandMatPoissonCreate a Random Matrix: Poisson
RandMatRFCreate a Random Matrix: Random Forest (RF)
RandMatTSpatchCreate a Random Matrix: ts-patch
RerFRerF forest Generator
RunFeatureImportanceCompute Feature Importance of a single RerF tree
RunOOBPredict class labels on out-of-bag observations using a...
RunPredictPredict class labels on a test set using a single tree.
RunPredictLeafCalculate similarity using a single tree.
StrCorrCompute tree strength and correlation
TwoMeansCutFind minimizing Two Means Cut for Vector
UrerfUnsupervised RerF forest Generator
neurodata/R-RerF documentation built on Dec. 6, 2018, 7:58 a.m.