Compute variable importance scores for the predictors in a model.
Usage
vi(object, ...)
# S3 method for default
vi(
object,
method = c("model", "firm", "permute", "shap"),
feature_names = NULL,
abbreviate_feature_names = NULL,
sort = TRUE,
decreasing = TRUE,
scale = FALSE,
rank = FALSE,
...
)
Arguments
- object
A fitted model object (e.g., a randomForest object) or an object that inherits from class
"vi"
.- ...
Additional optional arguments to be passed on to
vi_model
,vi_firm
,vi_permute
, orvi_shap
; see their respective help pages for details.- method
Character string specifying the type of variable importance (VI) to compute. Current options are
"model"
(the default), for model-specific VI scores (seevi_model
for details),"firm"
, for variance-based VI scores (seevi_firm
for details),"permute"
, for permutation-based VI scores (see 'vi_permute
for details), or"shap"
, for Shapley-based VI scores. For more details on the variance-based methods, see Greenwell et al. (2018) and Scholbeck et al. (2019).- feature_names
Character string giving the names of the predictor variables (i.e., features) of interest.
- abbreviate_feature_names
Integer specifying the length at which to abbreviate feature names. Default is
NULL
which results in no abbreviation (i.e., the full name of each feature will be printed).- sort
Logical indicating whether or not to order the sort the variable importance scores. Default is
TRUE
.- decreasing
Logical indicating whether or not the variable importance scores should be sorted in descending (
TRUE
) or ascending (FALSE
) order of importance. Default isTRUE
.- scale
Logical indicating whether or not to scale the variable importance scores so that the largest is 100. Default is
FALSE
.- rank
Logical indicating whether or not to rank the variable importance scores (i.e., convert to integer ranks). Default is
FALSE
. Potentially useful when comparing variable importance scores across different models using different methods.
Value
A tidy data frame (i.e., a "tibble"
object) with at least two
columns: Variable
and Importance
. For "lm"/"glm"
-like
objects, an additional column, called Sign
, is also included which
includes the sign (i.e., POS/NEG) of the original coefficient. If
method = "permute"
and nsim > 1
, then an additional column,
StDev
, giving the standard deviation of the permutation-based
variable importance scores is included.
References
Greenwell, B. M., Boehmke, B. C., and McCarthy, A. J. A Simple and Effective Model-Based Variable Importance Measure. arXiv preprint arXiv:1805.04755 (2018).
Examples
#
# A projection pursuit regression example
#
# Load the sample data
data(mtcars)
# Fit a projection pursuit regression model
mtcars.ppr <- ppr(mpg ~ ., data = mtcars, nterms = 1)
# Prediction wrapper that tells vi() how to obtain new predictions from your
# fitted model
pfun <- function(object, newdata) predict(object, newdata = newdata)
# Compute permutation-based variable importance scores
set.seed(1434) # for reproducibility
(vis <- vi(mtcars.ppr, method = "permute", target = "mpg", nsim = 10,
metric = "rmse", pred_wrapper = pfun))
#> # A tibble: 10 × 3
#> Variable Importance StDev
#> <chr> <dbl> <dbl>
#> 1 wt 3.17 0.374
#> 2 hp 2.18 0.462
#> 3 gear 0.755 0.367
#> 4 qsec 0.674 0.240
#> 5 cyl 0.462 0.158
#> 6 am 0.173 0.144
#> 7 vs 0.0999 0.0605
#> 8 drat 0.0265 0.0564
#> 9 carb 0.00898 0.00885
#> 10 disp -0.000824 0.00744
# Plot variable importance scores
vip(vis, include_type = TRUE, all_permutations = TRUE,
geom = "point", aesthetics = list(color = "forestgreen", size = 3))
#
# A binary classification example
#
if (FALSE) {
library(rpart) # for classification and regression trees
# Load Wisconsin breast cancer data; see ?mlbench::BreastCancer for details
data(BreastCancer, package = "mlbench")
bc <- subset(BreastCancer, select = -Id) # for brevity
# Fit a standard classification tree
set.seed(1032) # for reproducibility
tree <- rpart(Class ~ ., data = bc, cp = 0)
# Prune using 1-SE rule (e.g., use `plotcp(tree)` for guidance)
cp <- tree$cptable
cp <- cp[cp[, "nsplit"] == 2L, "CP"]
tree2 <- prune(tree, cp = cp) # tree with three splits
# Default tree-based VIP
vip(tree2)
# Computing permutation importance requires a prediction wrapper. For
# classification, the return value depends on the chosen metric; see
# `?vip::vi_permute` for details.
pfun <- function(object, newdata) {
# Need vector of predicted class probabilities when using log-loss metric
predict(object, newdata = newdata, type = "prob")[, "malignant"]
}
# Permutation-based importance (note that only the predictors that show up
# in the final tree have non-zero importance)
set.seed(1046) # for reproducibility
vi(tree2, method = "permute", nsim = 10, target = "Class",
metric = "logloss", pred_wrapper = pfun, reference_class = "malignant")
# Equivalent (but not sorted)
set.seed(1046) # for reproducibility
vi_permute(tree2, nsim = 10, target = "Class", metric = "logloss",
pred_wrapper = pfun, reference_class = "malignant")
}