varimp {MachineShop} | R Documentation |
Variable Importance
Description
Calculate measures of the relative importance of predictors in a model.
Usage
varimp(object, method = c("permute", "model"), scale = TRUE, ...)
Arguments
object |
model fit result.
|
method |
character string specifying the calculation of variable
importance as permutation-base ("permute" ) or model-specific
("model" ). If model-specific importance is specified but not
defined, the permutation-based method will be used instead with its default
values (below). Permutation-based variable importance is defined as the
relative change in model predictive performances between datasets with and
without permuted values for the associated variable (Fisher et al. 2019).
|
scale |
logical indicating whether importance values should be scaled to
a maximum of 100.
|
... |
arguments passed to model-specific or permutation-based variable
importance functions. These include the following arguments and default
values for method = "permute" .
select = NULL expression indicating predictor variables for
which to compute variable importance (see subset
for syntax) [default: all].
samples = 1 number of times to permute the values of each
variable. Larger numbers of samples decrease variability in the
estimates at the expense of increased computation time.
prop = numeric() proportion of observations to sample
without replacement at each round of variable permutations [default:
all]. Subsampling of observations can decrease computation time.
size = integer() number of observations to sample at each
round of permutations [default: all].
times = numeric() numeric vector of follow-up times at
which to predict survival probabilities or NULL for predicted
survival means.
metric = NULL metric function or function
name with which to calculate performance. If not specified, the first
applicable default metric from the performance functions is
used.
compare = c("-", "/") character specifying the relative
change to compute in comparing model predictive performances between
datasets with and without permuted values. The choices are difference
("-" ) and ratio ("/" ).
stats = MachineShop::settings("stat.TrainingParams") -
function, function name, or vector of these with which to compute
summary statistics on the set of variable importance values from the
permuted datasets.
na.rm = TRUE logical indicating whether to exclude missing
variable importance values from the calculation of summary statistics.
progress = TRUE logical indicating whether to display
iterative progress during computation.
|
Value
VariableImportance
class object.
References
Fisher, A., Rudin, C., & Dominici, F. (2019). All models are wrong, but many
are useful: Learning a variable's importance by studying an entire class of
prediction models simultaneously. Journal of Machine Learning
Research, 20, 1-81.
See Also
plot
Examples
## Requires prior installation of suggested package gbm to run
## Survival response example
library(survival)
gbm_fit <- fit(Surv(time, status) ~ ., data = veteran, model = GBMModel)
(vi <- varimp(gbm_fit))
plot(vi)
[Package
MachineShop version 3.7.0
Index]