inst/doc/ParallelForest-intro.R

In ParallelForest: Random Forest Classification with Parallel Computing

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library(ParallelForest)

data(low_high_earners)       # cleaned and prepared training dataset
data(low_high_earners_test)  # cleaned and prepared testing dataset

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fforest = grow.forest(Y~., data=low_high_earners)

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fforest["min_node_obs"]

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fforest["max_depth"]

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fforest["numsamps"]

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fforest["numvars"]

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fforest["numboots"]

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fforest_samepred = predict(fforest, low_high_earners)
pctcorrect_samepred = sum(low_high_earners$Y==fforest_samepred)/nrow(low_high_earners)
print(pctcorrect_samepred)

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fforest_newpred = predict(fforest, low_high_earners_test)
pctcorrect_newpred = sum(low_high_earners$Y==fforest_newpred)/nrow(low_high_earners)
print(pctcorrect_newpred)