Description Usage Arguments Value Examples
Variable importance is calculated as the difference between the min and max of the partial dependency output for a given feature
1 | loop_calculate_pd_vimp(pd_list, vimp_colname = "ensemble")
|
pd_list |
output from |
vimp_colname |
name of model (taken from the column names in |
a list the length of pd_list
, containing the variable importance
of each feature
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ## Not run:
# Example output from loop_calculate_partial_dependency
pd <- list(a = data.table(feature = c("a", "a", "a"),
feature_val = c(1, 3.5, 6),
model1 = c(-2.5, 0, 2.5),
model2 = c(0, 0, 0),
ensemble = c(-2.5, -0.75, 0)),
b = data.table(feature = c("b", "b", "b"),
feature_val = c(2, 3, 4),
model1 = c(0, 0, 0),
model2 = c(0, 0, 0),
ensemble = c(1, 2, 3)))
loop_calculate_pd_vimp(pd, vimp_colname = "ensemble")
## End(Not run)
|
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