randomForest: Classification and Regression with Random Forest

In Fuzzy-Logix/AdapteR: Converts R syntax to SQL to provide fast analytics using in-database C++ library DB Lytix.

Description

Random forest is a technique for reducing the variance of an estimated prediction function. It takes multiple random samples(with replacement) from the training data set, uses each of these samples to construct a separate model and separate predictions for test set, and then averages them.

Usage

randomForest(data, formula, ...)

Arguments

data	FLTable
formula	formula specifying the independent and dependent variable columns
ntree	Number of trees to grow. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times.
mtry	Number of variables randomly sampled as candidates at each split
nodesize	Minimum size of terminal nodes.
maxdepth	The maximum depth to which the tree can go. cp: Complexity parameter

Value

An object of class "FLRandomForest" containing the forest structure details.

See Also

randomForest for corresponding R function reference.

Examples

flt<-FLTable("tblDecisionTreeMulti","ObsID","VarID","Num_Val")
flobj<-randomForest(data = flt, formula = -1~., ntree=5)
print(flobj)
pred <- predict(flobj, newdata= flt[1:100,])
pred
plot(flobj)

Fuzzy-Logix/AdapteR documentation built on May 6, 2019, 5:07 p.m.