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, or vi_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 (see vi_model for details).`"firm"`

, for variance-based VI scores (see vi_firm for details).`"permute"`

, for permutation-based VI scores (see vi_permute for details).`"shap"`

, for Shapley-based VI scores (see vi_shap for details).

- 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 is`TRUE`

.- 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 two columns:

`Variable`

- the corresponding feature name;`Importance`

- the associated importance, computed as the average change in performance after a random permutation (or permutations, if`nsim > 1`

) of the feature in question.

For lm/glm-like objects, whenever
`method = "model"`

, the sign (i.e., POS/NEG) of the original coefficient is
also included in a column called `Sign`

.

If `method = "permute"`

and `nsim > 1`

, then an additional column (`StDev`

)
containing the standard deviation of the individual permutation scores for
each feature is also returned; this helps assess the stability/variation of
the individual permutation importance for each feature.

## 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, train = mtcars))
#> # 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", train = bc,
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")
}
```