View source: R/interaction_strength.R
interaction_strength | R Documentation |
Compute two-way feature interaction strength based on Friedman's H-statistic.
interaction_strength(pd_2d, pd_1d)
pd_2d |
Data frame containing the 2D partial dependence as returned by
|
pd_1d |
List of data frames containing the 1D partial dependence for
var1 and var2 as returned by |
A numeric value between 0 and 1 indicating interaction strength.
## Not run:
data('mtpl_be')
features <- setdiff(names(mtpl_be), c('id', 'nclaims', 'expo', 'long', 'lat'))
set.seed(12345)
gbm_fit <- gbm::gbm(as.formula(paste('nclaims ~',
paste(features, collapse = ' + '))),
distribution = 'poisson',
data = mtpl_be,
n.trees = 50,
interaction.depth = 3,
shrinkage = 0.1)
gbm_fun <- function(object, newdata) mean(predict(object, newdata, n.trees = object$n.trees, type = 'response'))
pd_2d <- get_pd(mfit = gbm_fit,
var = 'ageph_coverage',
grid = tidyr::expand_grid('ageph' %>% get_grid(data = mtpl_be),
'coverage' %>% get_grid(data = mtpl_be)),
data = mtpl_be,
subsample = 10000,
fun = gbm_fun)
pd_1d <- list(get_pd(mfit = gbm_fit,
var = 'ageph',
grid = 'ageph' %>% get_grid(data = mtpl_be),
data = mtpl_be,
subsample = 10000,
fun = gbm_fun),
get_pd(mfit = gbm_fit,
var = 'coverage',
grid = 'coverage' %>% get_grid(data = mtpl_be),
data = mtpl_be,
subsample = 10000,
fun = gbm_fun))
interaction_strength(pd_2d, pd_1d)
## End(Not run)
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