View source: R/information_gain.R
ComputeMaxInfoGainsDiscrete | R Documentation |
Max information gains (discrete)
ComputeMaxInfoGainsDiscrete( data, decision = NULL, dimensions = 1, pc.xi = 0.25, return.tuples = FALSE, return.min = 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 |
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 |
return.min |
whether to return min instead of max (per tuple) - not supported with CUDA |
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 |
If decision
is omitted, this function calculates either the variable entropy
(in 1D) or mutual information (in higher dimensions).
Translate "IG" respectively to entropy or mutual information in the
rest of this function's description.
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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