FeatureImp computes feature importance for prediction models. The importance is measured as the factor by which the model's prediction error increases when the feature is shuffled.


To compute the feature importance for a single feature, the model prediction loss (error) is measured before and after shuffling the values of the feature. By shuffling the feature values, the association between the outcome and the feature is destroyed. The larger the increase in prediction error, the more important the feature was. The shuffling is repeated to get more accurate results, since the permutation feature importance tends to be quite unstable. Read the Interpretable Machine Learning book to learn about feature importance in detail: https://christophm.github.io/interpretable-ml-book/feature-importance.html

The loss function can be either specified via a string, or by handing a function to FeatureImp(). If you want to use your own loss function it should have this signature:

function(actual, predicted)

Using the string is a shortcut to using loss functions from the Metrics package. Only use functions that return a single performance value, not a vector. Allowed losses are: "ce", "f1", "logLoss", "mae", "mse", "rmse", "mape", "mdae", "msle", "percent_bias", "rae", "rmse", "rmsle", "rse", "rrse" and "smape".

See library(help = "Metrics") to get a list of functions.


Parallelization is supported via package future. To initialize future-based parallelization, select an appropriate backend and specify the amount of workers. For example, to use a PSOCK based cluster backend do:

future::plan(multisession, workers = 2)
<iml function here>

Consult the resources of the future package for more parallel backend options.


Fisher, A., Rudin, C., and Dominici, F. (2018). Model Class Reliance: Variable Importance Measures for any Machine Learning Model Class, from the "Rashomon" Perspective. Retrieved from http://arxiv.org/abs/1801.01489

Super class

iml::InterpretationMethod -> FeatureImp

Public fields


(character(1) | function)
The loss function. Either the name of a loss (e.g. "ce" for classification or "mse") or a function.


The loss of the model before perturbing features.


Number of repetitions.


Either "ratio" or "difference", depending on whether the importance was calculated as difference between original model error and model error after permutation or as ratio.


Features for which importance scores are to be calculated. The names are the feature/group names, while the contents specify which feature(s) are to be permuted.


Inherited methods

Method new()

Create a FeatureImp object


  compare = "ratio",
  n.repetitions = 5,
  features = NULL



The object (created with Predictor$new()) holding the machine learning model and the data.


(character(1) | function)
The loss function. Either the name of a loss (e.g. "ce" for classification or "mse") or a function. See Details for allowed losses.


Either "ratio" or "difference". Should importance be measured as the difference or as the ratio of original model error and model error after permutation?

  • Ratio: error.permutation/error.orig

  • Difference: error.permutation - error.orig


How often should the shuffling of the feature be repeated? The higher the number of repetitions the more stable and accurate the results become.


(character or list)
For which features do you want importance scores calculated. The default value of NULL implies all features. Use a named list of character vectors to define groups of features for which joint importance will be calculated. See examples.


data.frame with the results of the feature importance computation. One row per feature with the following columns:

  • importance.05 (5% quantile of importance values from the repetitions)

  • importance (median importance)

  • importance.95 (95% quantile) and the permutation.error (median error over all repetitions).

The distribution of the importance is also visualized as a bar in the plots, the median importance over the repetitions as a point.

Method clone()

The objects of this class are cloneable with this method.


FeatureImp$clone(deep = FALSE)



Whether to make a deep clone.


# We train a tree on the Boston dataset:
data("Boston", package = "MASS")
tree <- rpart(medv ~ ., data = Boston)
y <- Boston$medv
X <- Boston[-which(names(Boston) == "medv")]
mod <- Predictor$new(tree, data = X, y = y)

# Compute feature importances as the performance drop in mean absolute error
imp <- FeatureImp$new(mod, loss = "mae")

# Plot the results directly

# Since the result is a ggplot object, you can extend it:
plot(imp) + theme_bw()

# If you want to do your own thing, just extract the data:
imp.dat <- imp$results
#>   feature importance.05 importance importance.95 permutation.error
#> 1      rm      2.090129   2.118682      2.212072          6.164733
#> 2   lstat      1.820267   1.869024      1.929110          5.438303
#> 3     dis      1.152082   1.177586      1.189817          3.426423
#> 4    crim      1.140340   1.157613      1.179885          3.368309
#> 5      zn      1.000000   1.000000      1.000000          2.909702
#> 6   indus      1.000000   1.000000      1.000000          2.909702
ggplot(imp.dat, aes(x = feature, y = importance)) +
  geom_point() +

# We can also look at the difference in model error instead of the ratio
imp <- FeatureImp$new(mod, loss = "mae", compare = "difference")

# Plot the results directly

# We can calculate feature importance for a subset of features 
imp <- FeatureImp$new(mod, loss = "mae", features = c("crim", "zn", "indus"))

# We can calculate joint importance of groups of features
groups = list(
 grp1 = c("crim", "zn", "indus", "chas"),
 grp2 = c("nox", "rm", "age", "dis"),
 grp3 = c("rad", "tax", "ptratio", "black", "lstat")
imp <- FeatureImp$new(mod, loss = "mae", features = groups)

# FeatureImp also works with multiclass classification.
# In this case, the importance measurement regards all classes
tree <- rpart(Species ~ ., data = iris)
X <- iris[-which(names(iris) == "Species")]
y <- iris$Species
mod <- Predictor$new(tree, data = X, y = y, type = "prob")

# For some models we have to specify additional arguments for the predict function
imp <- FeatureImp$new(mod, loss = "ce")

# For multiclass classification models, you can choose to only compute
# performance for one class.
# Make sure to adapt y
mod <- Predictor$new(tree,
  data = X, y = y == "virginica",
  type = "prob", class = "virginica"
imp <- FeatureImp$new(mod, loss = "ce")