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Implementation of Breiman's random forest algorithm (based on Breiman and Cutler's original Fortran code) for classification and regression.
RandomForestModel( ntree = 500, mtry = .(if (is.factor(y)) floor(sqrt(nvars)) else max(floor(nvars/3), 1)), replace = TRUE, nodesize = .(if (is.factor(y)) 1 else 5), maxnodes = integer() )
MLModel class object.
MLModel
number of trees to grow.
number of variables randomly sampled as candidates at each split.
should sampling of cases be done with or without replacement?
minimum size of terminal nodes.
maximum number of terminal nodes trees in the forest can have.
factor, numeric
factor
numeric
mtry, nodesize*
mtry
nodesize
* excluded from grids by default
Default argument values and further model details can be found in the source See Also link below.
randomForest, fit, resample
randomForest
fit
resample
# \donttest{ ## Requires prior installation of suggested package randomForest to run fit(sale_amount ~ ., data = ICHomes, model = RandomForestModel) # }
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