ComputeMaxInfoGainsDiscrete: Max information gains (discrete)

In MDFS: MultiDimensional Feature Selection

Max information gains (discrete)

Description

Max information gains (discrete)

Usage

ComputeMaxInfoGainsDiscrete(
  data,
  decision,
  contrast_data = NULL,
  dimensions = 1,
  pc.xi = 0.25,
  return.tuples = FALSE,
  interesting.vars = vector(mode = "integer"),
  require.all.vars = FALSE
)

Arguments

data	input data where columns are variables and rows are observations (all discrete with the same number of categories)
decision	decision variable as a binary sequence of length equal to number of observations
contrast_data	the contrast counterpart of data, has to have the same number of observations
dimensions	number of dimensions (a positive integer; 5 max)
pc.xi	parameter xi used to compute pseudocounts (the default is recommended not to be changed)
return.tuples	whether to return tuples where max IG was observed (one tuple per variable) - not supported with CUDA nor in 1D
interesting.vars	variables for which to check the IGs (none = all) - not supported with CUDA
require.all.vars	boolean whether to require tuple to consist of only interesting.vars

Value

A data.frame with the following columns:

• IG – max information gain (of each variable)

• Tuple.1, Tuple.2, ... – corresponding tuple (up to dimensions columns, available only when return.tuples == T)

• Discretization.nr – always 1 (for compatibility with the non-discrete function; available only when return.tuples == T)

Additionally attribute named run.params with run parameters is set on the result.

Examples

ComputeMaxInfoGainsDiscrete(madelon$data > 500, madelon$decision, dimensions = 2)

MDFS documentation built on May 31, 2023, 7:31 p.m.