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Tip revision: 583e8e137abae0107d3e0ea9b5549b3d141ba1c5 authored by Piotr Romanski on 11 April 2009, 00:00 UTC
version 0.17
Tip revision: 583e8e1
\title{ RandomForest filter }
  The algorithm finds weights of attributes using RandomForest algorithm.
random.forest.importance(formula, data, importance.type = 1)
  \item{formula}{ a symbolic description of a model }
  \item{data}{ data to process }
  \item{importance.type}{ either 1 or 2, specifying the type of importance measure (1=mean decrease in accuracy, 2=mean decrease in node impurity) }
  This is a wrapper for \code{\link[randomForest]{importance}}
a data.frame containing the worth of attributes in the first column and their names as row names
\author{ Piotr Romanski }
  weights <- random.forest.importance(Class~., HouseVotes84, importance.type = 1)
  subset <- cutoff.k(weights, 5)
  f <- as.simple.formula(subset, "Class")
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