random.forest.importance: RandomForest filter

In FSelector: Selecting attributes

Description

The algorithm finds weights of attributes using RandomForest algorithm.

Usage

random.forest.importance(formula, data, importance.type = 1)

Arguments

formula	a symbolic description of a model
data	data to process
importance.type	either 1 or 2, specifying the type of importance measure (1=mean decrease in accuracy, 2=mean decrease in node impurity)

Details

This is a wrapper for importance.

Value

a data.frame containing the worth of attributes in the first column and their names as row names

Author(s)

Piotr Romanski

Examples

  library(mlbench)
  data(HouseVotes84)
  
  weights <- random.forest.importance(Class~., HouseVotes84, importance.type = 1)
  print(weights)
  subset <- cutoff.k(weights, 5)
  f <- as.simple.formula(subset, "Class")
  print(f)

Example output

OpenJDK 64-Bit Server VM warning: Can't detect initial thread stack location - find_vma failed
    attr_importance
V1       -0.9326472
V2        5.3516958
V3       24.6220111
V4       73.3759457
V5       20.8561550
V6        0.1323011
V7        7.6021927
V8       10.6786898
V9        8.1278928
V10       0.9014629
V11      18.9199869
V12      13.3975239
V13      14.1095754
V14      14.3725155
V15       9.6665340
V16      -4.8176872
Class ~ V4 + V3 + V5 + V11 + V14
<environment: 0x2daf720>
Warning message:
system call failed: Cannot allocate memory 

FSelector documentation built on May 2, 2019, 4:52 p.m.