gRIT | R Documentation |
Run RIT across decision paths of a fitted random forest.
gRIT(
x,
y,
rand.forest = NULL,
read.forest = NULL,
rit.param = list(depth = 5, ntree = 500, nchild = 2, class.id = 1, min.nd = 1,
class.cut = NULL),
varnames.grp = colnames(x),
weights = rep(1, nrow(x)),
signed = TRUE,
oob.importance = TRUE,
ints.idx.eval = NULL,
ints.eval = NULL,
n.core = 1
)
x |
numeric feature matrix |
y |
response vector. If factor, classification is assumed. |
rand.forest |
an object of class randomForest. Required if read.forest is NULL. |
read.forest |
output of readForest. Required if rand.forest is NULL. |
rit.param |
named list specifying RIT parameters. Entries include
|
varnames.grp |
grouping "hyper-features" for RIT search. Features with the same name will be treated as identical for interaction search. |
weights |
numeric weight for each observation. Leaf nodes will be sampled for RIT with probability proprtional to the total weight of observations they contain. |
signed |
if TRUE, signed interactions will be returned |
oob.importance |
if TRUE, importance measures are evaluated on OOB samples. |
ints.idx.eval |
like |
ints.eval |
interactions to evaluate. If specified, importance metrics will be evaluated for these interactions instead of those recovered by RIT. |
n.core |
number of cores to use. If -1, all available cores are used. |
a data table containing the recovered interactions and importance scores.
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