interact.gbm {gbm} | R Documentation |
Computes Friedman's H-statistic to assess the strength of variable interactions.
interact.gbm(x, data, i.var = 1, n.trees = x$n.trees)
x |
a gbm.object fitted using a call to gbm |
data |
the dataset used to construct x . If the original dataset is
large, a random subsample may be used to accelerate the computation in
interact.gbm |
i.var |
a vector of indices or the names of the variables for compute
the interaction effect. If using indices, the variables are indexed in the
same order that they appear in the initial gbm formula. |
n.trees |
the number of trees used to generate the plot. Only the first
n.trees trees will be used |
interact.gbm
computes Friedman's H-statistic to assess the relative
strength of interaction effects in non-linear models. H is on the scale of
[0-1] with higher values indicating larger interaction effects. To connect to
a more familiar measure, if x_1 and x_2 are uncorrelated covariates
with mean 0 and variance 1 and the model is of the form
y=β_0+β_1x_1+β_2x_2+β_3x_3
then
H=frac{β_3}{sqrt{β_1^2+β_2^2+β_3^2}}
Returns the value of H.
Greg Ridgeway gregr@rand.org
J.H. Friedman and B.E. Popescu (2005). “Predictive Learning via Rule Ensembles.” Section 8.1