feamiR (version 0.1.0)

dtreevoting: Decision tree voting scheme. Implements a feature selection approach based on Decision Trees, using a voting scheme across the top levels on trees trained on multiple subsamples.

Description

Decision tree voting scheme. Implements a feature selection approach based on Decision Trees, using a voting scheme across the top levels on trees trained on multiple subsamples.

Usage

dtreevoting(num_runs = 100, num_levels = 10, file_path = file_path)

Arguments

num_runs

Number of subsamples to use for voting scheme (default: 100)

num_levels

Number of levels in each tree to consider. Only the features which appear in the top num_levels levels of the trees (from the root) will be counted

file_path

Where the num_runs subsample files are found (e.g. if sample 10 is at 'subsamples/sample10.csv' then file_path should be 'subsamples/sample'). There must be enough samples to fulfill num_runs runs.

Value

Outputs a dataframe containing (first column) total number of appearances of each feature (each row is a feature). The rest of the columns represent 1 run each and contain the level at which the feature appears.

Examples

# NOT RUN {
dtreevoting(
  num_runs=5,
  num_levels=10,
  file_path=paste(system.file('samples/subsamples', package = "feamiR"),'/sample',sep=''))
# }