View source: R/information_gain.R
ComputeMaxInfoGainsDiscrete | R Documentation |
Max information gains (discrete)
ComputeMaxInfoGainsDiscrete(
data,
decision,
contrast_data = NULL,
dimensions = 1,
pc.xi = 0.25,
return.tuples = FALSE,
interesting.vars = vector(mode = "integer"),
require.all.vars = FALSE
)
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 |
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.
ComputeMaxInfoGainsDiscrete(madelon$data > 500, madelon$decision, dimensions = 2)
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